Estimated Notebook Runtime: 2m 19s
import numpy as np
import pandas as pd
data = pd.read_csv('../data/stock_data.csv')
data.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 340 entries, 0 to 339 Data columns (total 15 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Ticker Symbol 340 non-null object 1 Security 340 non-null object 2 GICS Sector 340 non-null object 3 GICS Sub Industry 340 non-null object 4 Current Price 340 non-null float64 5 Price Change 340 non-null float64 6 Volatility 340 non-null float64 7 ROE 340 non-null int64 8 Cash Ratio 340 non-null int64 9 Net Cash Flow 340 non-null int64 10 Net Income 340 non-null int64 11 Earnings Per Share 340 non-null float64 12 Estimated Shares Outstanding 340 non-null float64 13 P/E Ratio 340 non-null float64 14 P/B Ratio 340 non-null float64 dtypes: float64(7), int64(4), object(4) memory usage: 40.0+ KB
object_list=[]
for col in data.columns:
if data[col].dtype == 'object':
object_list.append(col)
# data = data.fillna(data.mode().iloc[0])
object_list
['Ticker Symbol', 'Security', 'GICS Sector', 'GICS Sub Industry']
# Libraries to help with reading and manipulating data
import numpy as np
import pandas as pd
# Libraries to help with data visualization
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_theme(style='darkgrid')
# Removes the limit for the number of displayed columns
pd.set_option("display.max_columns", None)
# Sets the limit for the number of displayed rows
pd.set_option("display.max_rows", 200)
# to scale the data using z-score
from sklearn.preprocessing import StandardScaler
# to compute distances
from scipy.spatial.distance import cdist, pdist
# to perform k-means clustering and compute silhouette scores
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score
# to visualize the elbow curve and silhouette scores
from yellowbrick.cluster import KElbowVisualizer, SilhouetteVisualizer
# to perform hierarchical clustering, compute cophenetic correlation, and create dendrograms
from sklearn.cluster import AgglomerativeClustering
from scipy.cluster.hierarchy import dendrogram, linkage, cophenet
# to suppress warnings
import warnings
warnings.filterwarnings("ignore")
runtime of above: ~ 2.3 to 52.6 s
# ChatGPT3 recommended libraries:
from scipy.spatial.distance import pdist, squareform
# https://docs.xlwings.org/en/latest/jupyternotebooks.html
from xlwings import view
from xlwings import load
data = pd.read_csv('../data/stock_data.csv')
data.shape[0] -> rows, data.shape[1] -> colsdata.head(2)
| Ticker Symbol | Security | GICS Sector | GICS Sub Industry | Current Price | Price Change | Volatility | ROE | Cash Ratio | Net Cash Flow | Net Income | Earnings Per Share | Estimated Shares Outstanding | P/E Ratio | P/B Ratio | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | AAL | American Airlines Group | Industrials | Airlines | 42.349998 | 9.999995 | 1.687151 | 135 | 51 | -604000000 | 7610000000 | 11.39 | 6.681299e+08 | 3.718174 | -8.784219 |
| 1 | ABBV | AbbVie | Health Care | Pharmaceuticals | 59.240002 | 8.339433 | 2.197887 | 130 | 77 | 51000000 | 5144000000 | 3.15 | 1.633016e+09 | 18.806350 | -8.750068 |
# checking shape of the data
print(f"There are {data.shape[0]} rows and {data.shape[1]} columns.") ## Complete the code to get the shape of data
There are 340 rows and 15 columns.
# let's view a sample of the data
data.sample(n=4, random_state=1)
| Ticker Symbol | Security | GICS Sector | GICS Sub Industry | Current Price | Price Change | Volatility | ROE | Cash Ratio | Net Cash Flow | Net Income | Earnings Per Share | Estimated Shares Outstanding | P/E Ratio | P/B Ratio | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 102 | DVN | Devon Energy Corp. | Energy | Oil & Gas Exploration & Production | 32.000000 | -15.478079 | 2.923698 | 205 | 70 | 830000000 | -14454000000 | -35.55 | 4.065823e+08 | 93.089287 | 1.785616 |
| 125 | FB | Information Technology | Internet Software & Services | 104.660004 | 16.224320 | 1.320606 | 8 | 958 | 592000000 | 3669000000 | 1.31 | 2.800763e+09 | 79.893133 | 5.884467 | |
| 11 | AIV | Apartment Investment & Mgmt | Real Estate | REITs | 40.029999 | 7.578608 | 1.163334 | 15 | 47 | 21818000 | 248710000 | 1.52 | 1.636250e+08 | 26.335526 | -1.269332 |
| 248 | PG | Procter & Gamble | Consumer Staples | Personal Products | 79.410004 | 10.660538 | 0.806056 | 17 | 129 | 160383000 | 636056000 | 3.28 | 4.913916e+08 | 24.070121 | -2.256747 |
# checking the column names and datatypes
data.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 340 entries, 0 to 339 Data columns (total 15 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Ticker Symbol 340 non-null object 1 Security 340 non-null object 2 GICS Sector 340 non-null object 3 GICS Sub Industry 340 non-null object 4 Current Price 340 non-null float64 5 Price Change 340 non-null float64 6 Volatility 340 non-null float64 7 ROE 340 non-null int64 8 Cash Ratio 340 non-null int64 9 Net Cash Flow 340 non-null int64 10 Net Income 340 non-null int64 11 Earnings Per Share 340 non-null float64 12 Estimated Shares Outstanding 340 non-null float64 13 P/E Ratio 340 non-null float64 14 P/B Ratio 340 non-null float64 dtypes: float64(7), int64(4), object(4) memory usage: 40.0+ KB
# copying the data to another variable to avoid any changes to original data
df = data.copy()
# checking for duplicate values
df.duplicated().sum() ## Complete the code to get total number of duplicate values
0
def isna_checker():
"""
function to check if the count in each col of data is 0.
"""
df_isna=df.isna().sum() # full dataframe
found_na=False
for i in df_isna.keys(): # take 1 key at a time out of just the keys
if df_isna==0:
pass
# inform user
if found_na==False:
print('no na values in data')
else:
print('the following columns have na values:')
Project milestones Creating and editing milestones for issues and pull requests
object_na_counts={}
df_isna=df.isna().sum()
dnaky=df_isna.keys()
dnavl=df_isna.array
for index, col_na_count in enumerate(df_isna):
# print(index)
object_na_counts.update({dnaky[index]:dnavl[index]})
object_na_counts
# reference: [pandas.DataFrame.values](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.values.html)
# reference: [pandas.Series.array](https://pandas.pydata.org/docs/reference/api/pandas.Series.array.html#pandas.Series.array)
# dnavl[0]
{'Ticker Symbol': 0,
'Security': 0,
'GICS Sector': 0,
'GICS Sub Industry': 0,
'Current Price': 0,
'Price Change': 0,
'Volatility': 0,
'ROE': 0,
'Cash Ratio': 0,
'Net Cash Flow': 0,
'Net Income': 0,
'Earnings Per Share': 0,
'Estimated Shares Outstanding': 0,
'P/E Ratio': 0,
'P/B Ratio': 0}
object_na_counts.keys()
dict_keys(['Ticker Symbol', 'Security', 'GICS Sector', 'GICS Sub Industry', 'Current Price', 'Price Change', 'Volatility', 'ROE', 'Cash Ratio', 'Net Cash Flow', 'Net Income', 'Earnings Per Share', 'Estimated Shares Outstanding', 'P/E Ratio', 'P/B Ratio'])
object_na_counts.values()
dict_values([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
dict.values returns dict-value object, not list object
Answer: If you want to get a list object, you need to convert it to list by passing the result to the list() function.
Code:
the_values = dict.values()
SUM = sum(list(the_values)[1:10])
Python: how to convert a dictionary into a subscriptable array?
sum_object_na_counts=0
for index, value in enumerate(object_na_counts.values()):
sum_object_na_counts+=list(object_na_counts.values())[index]
if (sum_object_na_counts==0):
print("no na values in dataframe")
no na values in dataframe
df.describe(include='all').T
| count | unique | top | freq | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Ticker Symbol | 340 | 340 | AAL | 1 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| Security | 340 | 340 | American Airlines Group | 1 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| GICS Sector | 340 | 11 | Industrials | 53 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| GICS Sub Industry | 340 | 104 | Oil & Gas Exploration & Production | 16 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| Current Price | 340.0 | NaN | NaN | NaN | 80.862345 | 98.055086 | 4.5 | 38.555 | 59.705 | 92.880001 | 1274.949951 |
| Price Change | 340.0 | NaN | NaN | NaN | 4.078194 | 12.006338 | -47.129693 | -0.939484 | 4.819505 | 10.695493 | 55.051683 |
| Volatility | 340.0 | NaN | NaN | NaN | 1.525976 | 0.591798 | 0.733163 | 1.134878 | 1.385593 | 1.695549 | 4.580042 |
| ROE | 340.0 | NaN | NaN | NaN | 39.597059 | 96.547538 | 1.0 | 9.75 | 15.0 | 27.0 | 917.0 |
| Cash Ratio | 340.0 | NaN | NaN | NaN | 70.023529 | 90.421331 | 0.0 | 18.0 | 47.0 | 99.0 | 958.0 |
| Net Cash Flow | 340.0 | NaN | NaN | NaN | 55537620.588235 | 1946365312.175789 | -11208000000.0 | -193906500.0 | 2098000.0 | 169810750.0 | 20764000000.0 |
| Net Income | 340.0 | NaN | NaN | NaN | 1494384602.941176 | 3940150279.327936 | -23528000000.0 | 352301250.0 | 707336000.0 | 1899000000.0 | 24442000000.0 |
| Earnings Per Share | 340.0 | NaN | NaN | NaN | 2.776662 | 6.587779 | -61.2 | 1.5575 | 2.895 | 4.62 | 50.09 |
| Estimated Shares Outstanding | 340.0 | NaN | NaN | NaN | 577028337.75403 | 845849595.417695 | 27672156.86 | 158848216.1 | 309675137.8 | 573117457.325 | 6159292035.0 |
| P/E Ratio | 340.0 | NaN | NaN | NaN | 32.612563 | 44.348731 | 2.935451 | 15.044653 | 20.819876 | 31.764755 | 528.039074 |
| P/B Ratio | 340.0 | NaN | NaN | NaN | -1.718249 | 13.966912 | -76.119077 | -4.352056 | -1.06717 | 3.917066 | 129.064585 |
Tabnine/GitHub CoPilot assisted sentence completion in the next couple of markdown cells.:
describe() method:¶(and .T to transpose dataframe for readability),
object_list
['Ticker Symbol', 'Security', 'GICS Sector', 'GICS Sub Industry']
For the 3 columns:
freq
all the numeric columns have NaN values.
Explanation: 3 cols not applicable to numerical columns.
count is an integer for object columns, and a float for numeric columns.
Price Change, Net Cash Flow and Net Income have negative values, (not an issue as these values can be negative).
What is the Price to Book Ratio (P/B Ratio)? :
"A negative book value means that a company has more total liabilities than assets." (Mathematically possible, so not an issue with the data.)
For the univariate analysis: histogram boxplot was used.
The first line of code in the function starts with:
f2, (ax_box2, ax_hist2) = plt.subplots(
Here is the documentation for matplotlib.pyplot.subplots
which states that the purpose of the method is to:
"Create a figure and a set of subplots.
This utility wrapper makes it convenient to create common layouts of subplots, including the enclosing figure object, in a single call.
fig :
Figure
ax :
Axes or array of Axes
ax can be either :
squeeze keyword, see above.Typical idioms for handling the return value are:
===================================================
# using the variable ax for single a Axes
fig, ax = plt.subplots()
# using the variable axs for multiple Axes
fig, axs = plt.subplots(2, 2)
# using tuple unpacking for multiple Axes
fig, (ax1, ax2) = plt.subplots(1, 2)
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)
(The names ax and pluralized axs
Reference: seaborn.boxplot / from VSCode mouseover function popup
seaborn.boxplot:
(function) def boxplot(
data: Any | None = None,
*,
x: Any | None = None,
y: Any | None = None,
hue: Any | None = None,
order: Any | None = None,
hue_order: Any | None = None,
orient: Any | None = None,
color: Any | None = None,
palette: Any | None = None,
saturation: float = 0.75,
width: float = 0.8,
dodge: bool = True,
fliersize: int = 5,
linewidth: Any | None = None,
whis: float = 1.5,
ax: Any | None = None,
**kwargs: Any
) -> Any
box plot (box-and-whisker plot):
shows distributions with respect to categories.
shows distribution of data for comparisons between variables. box shows quartiles of data, whiskers extend, show rest of distribution, except “outliers” found by a method that is a function of the inter-quartile range.
================================================================================
Reference: seaborn.histplot / from VSCode mouseover function popup:
(function) def histplot(
data: Any | None = None,
*,
x: Any | None = None,
y: Any | None = None,
hue: Any | None = None,
weights: Any | None = None,
stat: str = "count",
bins: str = "auto",
binwidth: Any | None = None,
binrange: Any | None = None,
discrete: Any | None = None,
cumulative: bool = False,
common_bins: bool = True,
common_norm: bool = True,
multiple: str = "layer",
element: str = "bars",
fill: bool = True,
shrink: int = 1,
kde: bool = False,
kde_kws: Any | None = None,
line_kws: Any | None = None,
thresh: int = 0,
pthresh: Any | None = None,
pmax: Any | None = None,
cbar: bool = False,
cbar_ax: Any | None = None,
cbar_kws: Any | None = None,
palette: Any | None = None,
hue_order: Any | None = None,
hue_norm: Any | None = None,
color: Any | None = None,
log_scale: Any | None = None,
legend: bool = True,
ax: Any | None = None,
**kwargs: Any
) -> Any
Plot univariate / bivariate histograms, show data distributions.
histogram: visualization of distribution of variable(s) by counting number of observations in bins
function can:
(More info in user guide)
present in code as: ax_hist2.axvline
Reference: -- | matplotlib.pyplot.axvline
Template: ## | matplotlib.pyplot.axvline(x=0, ymin=0, ymax=1, **kwargs)
Description: -| Add a vertical line across the Axes.
x float, default: 0 x position in data coordinates of the vertical line.
ymin float, default: 0 Should be between 0 and 1, 0 being the bottom of the plot, 1 the top of the plot.
ymax float, default: 1 Should be between 0 and 1, 0 being the bottom of the plot, 1 the top of the plot.
Line2D
**kwargs Valid keyword arguments are Line2D properties, except for 'transform':
Property | # | Description
color or c | # | color
linestyle or ls | {'-', '--', '-.', ':', '', (offset, on-off-seq), ...}
# function to plot a boxplot and a histogram along the same scale.
def histogram_boxplot(df, feature, figsize=(8, 5), kde=False, bins=None):
"""
Boxplot and histogram combined
data: dataframe
feature: dataframe column
figsize: size of figure (default (12,7))
kde: whether to the show density curve (default False)
bins: number of bins for histogram (default None)
"""
f2, (ax_box2, ax_hist2) = plt.subplots(
nrows=2, # Number of rows of the subplot grid= 2
sharex=True, # x-axis will be shared among all subplots
gridspec_kw={"height_ratios": (0.25, 0.75)},
figsize=figsize,
) # creating the 2 subplots
sns.boxplot(
data=df, x=feature, ax=ax_box2, showmeans=True, color="violet"
) # boxplot will be created and a star will indicate the mean value of the column
sns.histplot(
data=df, x=feature, kde=kde, ax=ax_hist2, bins=bins, palette="winter"
) if bins else sns.histplot(
data=df, x=feature, kde=kde, ax=ax_hist2
) # For histogram
ax_hist2.axvline(
df[feature].mean(), color="green", linestyle="--"
) # Add mean to the histogram
ax_hist2.axvline(
df[feature].median(), color="black", linestyle="-"
) # Add median to the histogram
output format: histogram with a single thin boxplot above to summarise the distribution.
Current Price¶histogram_boxplot(df, 'Current Price')
df['Current Price'].describe() # GitHub Copilot assisted line
count 340.000000 mean 80.862345 std 98.055086 min 4.500000 25% 38.555000 50% 59.705000 75% 92.880001 max 1274.949951 Name: Current Price, dtype: float64
.describe()
used for calculating some statistical data like percentile, mean and std of the numerical values of the Series or DataFrame
Describe,sets%20of%20mixed%20data%20types.)
general form: DataFrame.describe(percentiles=None, include=None, exclude=None)
Generate descriptive statistics.
Analyzes:
Output varies with input. See notes in docs,sets%20of%20mixed%20data%20types.) for more detail.
percentiles : list-like of numbers, optional
include : ‘all’, list-like of dtypes or None (default), optional
Here are the options:
‘all’ : All columns of the input will be included in the output.
A list-like of dtypes : Limits the results to the provided data types. To limit the result to numeric types submit numpy.number. To limit it instead to object columns submit the numpy.object data type. Strings can also be used in the style of select_dtypes (e.g. df.describe(include=['O'])). To select pandas categorical columns, use 'category'
None (default) : The result will include all numeric columns.
df['Current Price'].describe()
count 340.000000 mean 80.862345 std 98.055086 min 4.500000 25% 38.555000 50% 59.705000 75% 92.880001 max 1274.949951 Name: Current Price, dtype: float64
df['Current Price'].describe().keys()
Index(['count', 'mean', 'std', 'min', '25%', '50%', '75%', 'max'], dtype='object')
df['Current Price'].describe().keys()[0]
'count'
IQR=[]
Q1=0
Q3=0
for x in df['Current Price'].describe().keys():
if x== '25%':
Q1=x
if x == '75%':
Q3=x
IQR=[Q1,Q3]
# df['Current Price'].describe().keys()[i]
Q1=IQR[0]
Q3=IQR[1]
# IQR_no=Q1-Q3
Q1
'25%'
for a in df['Current Price'].describe().keys():
if a=='25%':
Q1_val=(df['Current Price'].describe()[a])
Q1_val
38.55499975
for a in df['Current Price'].describe().keys():
if a=='75%':
Q3_val=(df['Current Price'].describe()[a])
Q3_val
92.88000124999999
IQR_val = Q3_val - Q1_val
IQR_val
54.32500149999999
df['Current Price'].describe()
count 340.000000 mean 80.862345 std 98.055086 min 4.500000 25% 38.555000 50% 59.705000 75% 92.880001 max 1274.949951 Name: Current Price, dtype: float64
df['Current Price'].describe()
count 340.000000 mean 80.862345 std 98.055086 min 4.500000 25% 38.555000 50% 59.705000 75% 92.880001 max 1274.949951 Name: Current Price, dtype: float64
round(Q1_val,3)
38.555
Q1_val_round_3_to_2=round(Q1_val,3)
round(Q1_val_round_3_to_2,2) # rounds to 38.56
38.56
round(Q1_val,2) # rounds to 38.55
38.55
Q3_val_round_3_to_2=round(Q3_val,3)
round(Q3_val_round_3_to_2,2)
92.88
round(round(Q3_val,3),2)
92.88
text: *args allows us to pass a variable number of non-keyword arguments to a Python function.
code:
def add(*args):
print(args, type(args))
add(2, 3)
IQR_val
54.32500149999999
def two_decimal_correct(*args):
"""
pass *args as list
takes floats
returns list_of_outputs rounded to 2 decimal places
Purpose of function:
- to avoid error rounding from float to 2 decimal places
- by first rounding to 3 and then to 2
- which seems to fix bug of:
- rounding directly to 2
- can result in rounding down
- even when next decimal place is >=5
"""
list_of_outputs=[]
for new_input in args:
new_output=round(round(new_input,3),2)
list_of_outputs.append(new_output)
return list_of_outputs
two_decimal_correct(Q3_val, Q1_val, IQR_val)
[92.88, 38.56, 54.32]
type(two_decimal_correct(Q3_val, Q1_val, IQR_val))
list
type(IQR_val)
numpy.float64
[IQR_val]
[54.32500149999999]
two_decimal_correct(IQR_val)
[54.32]
TODO: How to unpack list.
Reference: How to Unpack a List in Python
Code:
colors = ['red', 'blue', 'green']
red = colors[0]
blue = colors[1]
green = colors[2]
two_decimal_correct(IQR_val)[0]
54.32
df['Current Price'].describe()
count 340.000000 mean 80.862345 std 98.055086 min 4.500000 25% 38.555000 50% 59.705000 75% 92.880001 max 1274.949951 Name: Current Price, dtype: float64
print('The interquartile range (IQR) (Q3 - Q1) ({Q3_val_2:.2f}-{Q1_val_2:.2f}) (92.88 - 38.56) is $'.format(Q3_val_2=two_decimal_correct(Q3_val)[0],Q1_val_2=two_decimal_correct(Q1_val)[0]),'{IQR_val_2:.2f}'.format(IQR_val_2=two_decimal_correct(IQR_val)[0]))
The interquartile range (IQR) (Q3 - Q1) (92.88-38.56) (92.88 - 38.56) is $ 54.32
# Some test data
np.random.seed(33454)
df = (
# A standard distribution
pd.DataFrame({'nb': np.random.randint(0, 100, 20)})
# Adding some outliers
.append(pd.DataFrame({'nb': np.random.randint(100, 200, 2)}))
# Reseting the index
.reset_index(drop=True)
)
# Computing IQR
Q1 = df['nb'].quantile(0.25)
Q3 = df['nb'].quantile(0.75)
IQR = Q3 - Q1
# Filtering Values between Q1-1.5IQR and Q3+1.5IQR
filtered = df.query('(@Q1 - 1.5 * @IQR) <= nb <= (@Q3 + 1.5 * @IQR)')
df['Current Price'].quantile(0.25)
38.55499975
As actual 1st quantile is 38.55499975, per df['Current Price'].quantile(0.25),
not 38.555000 per df['Current Price'].describe[4],
function: two_decimal_correct() is not needed,
as this function worked from the false assumption that
df['Current Price'].describe[4] was precisely correct to the number of decimal places shown.
However, it now appears that the reason the value was rounded down was that the actual value
was just under 38.555000 quoted by df['Current Price']
which explained why it rounded down for both {x.:f}.format() and round(x,2) formulae.
bear in mind that there are 15 different ways to calculate quartiles.. so look under the hood as different functions may give slightly different results (pandas vs numpy vs scipy..) jse.amstat.org/v14n3/langford.html [1], [2]
In this paper, I will discuss the various methods, and using a precise definition (Definition 2) of percentile, identify that method which satisfies this definition. Unfortunately this method (the “CDF Method”) is not, in its usual form, the easiest for a student to apply. [2]
search: the CDF Method for quantiles
reference: The Inverse CDF Method
explanation:
This ... math and probability post ... will talk about generating non-uniform random variables/numbers with the Inverse CDF Method [, which] involves computing quantiles from probabilities and using standard uniform random variables to generate non-uniform random variables.
This topic relates to Probability Theory, and Monte Carlo Simulations.
[1]
Aside: [1] references [2]
[1] Quartiles in Elementary Statistics
Price Change¶histogram_boxplot(df, 'Price Change') ## Complete the code to create histogram_boxplot for 'Price Change'
df['Price Change'].describe()
count 340.000000 mean 4.078194 std 12.006338 min -47.129693 25% -0.939484 50% 4.819505 75% 10.695493 max 55.051683 Name: Price Change, dtype: float64
df['Price Change'].mean() = 4.08
floatNumber = 1.9876
print("%.1f" % floatNumber)
# 2.0
Want to cast to float not int To convert, or cast, a string to an integer in Python, you use the int() built-in function. Python Convert String to Int – How to Cast a String in Python%20.)
float("%.2f" % df['Price Change'].mean()) # resolved: cast string to float
4.08
print("mean of Price Change was $", float("%.2f" % df['Price Change'].mean()), sep='')
mean of Price Change was $4.08
print("median of Price Change was $", float("%.2f" % df['Price Change'].median()), sep='')
median of Price Change was $4.82
print("standard deviation for Price Change was $", float("%.2f" % df['Price Change'].std()), sep='')
standard deviation for Price Change was $12.01
print("minimum Price Change was $", float("%.2f" % df['Price Change'].min()), sep='')
minimum Price Change was $-47.13
print("maximum Price Change was $", float("%.2f" % df['Price Change'].max()), sep='')
maximum Price Change was $55.05
As median was slightly to the right of the mean, or mean left of median, the histogram was slightly left skewed, with slightly longer left tail, with more values in the tail further from the center.
This would indicate that there were more extremes of loss than of gain of these stocks over the 13 week period.
Volatility¶feature='Volatility'
histogram_boxplot(df=df,feature='Volatility') ## Complete the code to create histogram_boxplot for 'Volatility'
def function_arb(a,y):
if y == '.mean()':
return a.mean()
elif y == '.median()':
return a.median()
elif y == '.std()':
return a.std()
elif y == '.min()':
return a.min()
elif y == '.max()':
return a.max()
for x, y in zip(['mean', 'median', 'standard deviation', 'minimum', 'maximum'], ['.mean()', '.median()', '.std()', '.min()', '.max()']):
a=df[feature]
returned=function_arb(a,y)
print(str("{x2} {feature_2} was $".format(x2=x,feature_2=feature)),float("%.2f" % returned))
mean Volatility was $ 1.53 median Volatility was $ 1.39 standard deviation Volatility was $ 0.59 minimum Volatility was $ 0.73 maximum Volatility was $ 4.58
ROE¶feature='ROE'
histogram_boxplot(df=df,feature=feature) ## Complete the code to create histogram_boxplot for 'ROE'
def function_arb(a,y):
if y == '.mean()':
return a.mean()
elif y == '.median()':
return a.median()
elif y == '.std()':
return a.std()
elif y == '.min()':
return a.min()
elif y == '.max()':
return a.max()
for x, y in zip(['mean', 'median', 'standard deviation', 'minimum', 'maximum'], ['.mean()', '.median()', '.std()', '.min()', '.max()']):
a=df[feature]
returned=function_arb(a,y)
print(str("{x2} {feature_2} was $".format(x2=x,feature_2=feature)),float("%.2f" % returned))
mean ROE was $ 39.6 median ROE was $ 15.0 standard deviation ROE was $ 96.55 minimum ROE was $ 1.0 maximum ROE was $ 917.0
Cash Ratio¶feature='Cash Ratio'
histogram_boxplot(df=df,feature=feature) ## Complete the code to create histogram_boxplot for 'Cash Ratio'
def function_arb(a,y):
if y == '.mean()':
return a.mean()
elif y == '.median()':
return a.median()
elif y == '.std()':
return a.std()
elif y == '.min()':
return a.min()
elif y == '.max()':
return a.max()
for x, y in zip(['mean', 'median', 'standard deviation', 'minimum', 'maximum'], ['.mean()', '.median()', '.std()', '.min()', '.max()']):
a=df[feature]
returned=function_arb(a,y)
print(str("{x2} {feature_2} was $".format(x2=x,feature_2=feature)),float("%.2f" % returned))
mean Cash Ratio was $ 70.02 median Cash Ratio was $ 47.0 standard deviation Cash Ratio was $ 90.42 minimum Cash Ratio was $ 0.0 maximum Cash Ratio was $ 958.0
Net Cash Flow¶feature='Net Cash Flow'
histogram_boxplot(df=df,feature=feature) ## Complete the code to create histogram_boxplot for 'Net Cash Flow'
def function_arb(a,y):
if y == '.mean()':
return a.mean()
elif y == '.median()':
return a.median()
elif y == '.std()':
return a.std()
elif y == '.min()':
return a.min()
elif y == '.max()':
return a.max()
for x, y in zip(['mean', 'median', 'standard deviation', 'minimum', 'maximum'], ['.mean()', '.median()', '.std()', '.min()', '.max()']):
a=df[feature]
returned=function_arb(a,y)
print(str("{x2} {feature_2} was $".format(x2=x,feature_2=feature)),float("%.2f" % returned))
mean Net Cash Flow was $ 55537620.59 median Net Cash Flow was $ 2098000.0 standard deviation Net Cash Flow was $ 1946365312.18 minimum Net Cash Flow was $ -11208000000.0 maximum Net Cash Flow was $ 20764000000.0
Net Income¶feature='Net Income'
histogram_boxplot(df=df,feature=feature) ## Complete the code to create histogram_boxplot for 'Net Income'
def function_arb(a,y):
if y == '.mean()':
return a.mean()
elif y == '.median()':
return a.median()
elif y == '.std()':
return a.std()
elif y == '.min()':
return a.min()
elif y == '.max()':
return a.max()
for x, y in zip(['mean', 'median', 'standard deviation', 'minimum', 'maximum'], ['.mean()', '.median()', '.std()', '.min()', '.max()']):
a=df[feature]
returned=function_arb(a,y)
print(str("{x2} {feature_2} was $".format(x2=x,feature_2=feature)),float("%.2f" % returned))
mean Net Income was $ 1494384602.94 median Net Income was $ 707336000.0 standard deviation Net Income was $ 3940150279.33 minimum Net Income was $ -23528000000.0 maximum Net Income was $ 24442000000.0
Earnings Per Share¶feature='Earnings Per Share'
histogram_boxplot(df=df,feature=feature) ## Complete the code to create histogram_boxplot for 'Earnings Per Share'
def function_arb(a,y):
if y == '.mean()':
return a.mean()
elif y == '.median()':
return a.median()
elif y == '.std()':
return a.std()
elif y == '.min()':
return a.min()
elif y == '.max()':
return a.max()
for x, y in zip(['mean', 'median', 'standard deviation', 'minimum', 'maximum'], ['.mean()', '.median()', '.std()', '.min()', '.max()']):
a=df[feature]
returned=function_arb(a,y)
print(str("{x2} {feature_2} was $".format(x2=x,feature_2=feature)),float("%.2f" % returned))
mean Earnings Per Share was $ 2.78 median Earnings Per Share was $ 2.9 standard deviation Earnings Per Share was $ 6.59 minimum Earnings Per Share was $ -61.2 maximum Earnings Per Share was $ 50.09
Estimated Shares Outstanding¶feature='Estimated Shares Outstanding'
histogram_boxplot(df=df,feature=feature) ## Complete the code to create histogram_boxplot for 'Estimated Shares Outstanding'
def function_arb(a,y):
if y == '.mean()':
return a.mean()
elif y == '.median()':
return a.median()
elif y == '.std()':
return a.std()
elif y == '.min()':
return a.min()
elif y == '.max()':
return a.max()
for x, y in zip(['mean', 'median', 'standard deviation', 'minimum', 'maximum'], ['.mean()', '.median()', '.std()', '.min()', '.max()']):
a=df[feature]
returned=function_arb(a,y)/10e6
print(str("{x2} {feature_2} was $".format(x2=x,feature_2=feature)),float("%.2f" % returned), ' million', sep='')
mean Estimated Shares Outstanding was $57.7 million median Estimated Shares Outstanding was $30.97 million standard deviation Estimated Shares Outstanding was $84.58 million minimum Estimated Shares Outstanding was $2.77 million maximum Estimated Shares Outstanding was $615.93 million
P/E Ratio¶feature='P/E Ratio'
histogram_boxplot(df=df,feature=feature) ## Complete the code to create histogram_boxplot for 'P/E Ratio'
def function_arb(a,y):
if y == '.mean()':
return a.mean()
elif y == '.median()':
return a.median()
elif y == '.std()':
return a.std()
elif y == '.min()':
return a.min()
elif y == '.max()':
return a.max()
for x, y in zip(['mean', 'median', 'standard deviation', 'minimum', 'maximum'], ['.mean()', '.median()', '.std()', '.min()', '.max()']):
a=df[feature]
returned=function_arb(a,y)
print(str("{x2} {feature_2} was $".format(x2=x,feature_2=feature)),float("%.2f" % returned), sep='')
mean P/E Ratio was $32.61 median P/E Ratio was $20.82 standard deviation P/E Ratio was $44.35 minimum P/E Ratio was $2.94 maximum P/E Ratio was $528.04
P/B Ratio¶feature='P/B Ratio'
histogram_boxplot(df=df,feature=feature) ## Complete the code to create histogram_boxplot for 'P/B Ratio'
def function_arb(a,y):
if y == '.mean()':
return a.mean()
elif y == '.median()':
return a.median()
elif y == '.std()':
return a.std()
elif y == '.min()':
return a.min()
elif y == '.max()':
return a.max()
for x, y in zip(['mean', 'median', 'standard deviation', 'minimum', 'maximum'], ['.mean()', '.median()', '.std()', '.min()', '.max()']):
a=df[feature]
returned=function_arb(a,y)
print(str("{x2} {feature_2} was $".format(x2=x,feature_2=feature)),float("%.2f" % returned), sep='')
mean P/B Ratio was $-1.72 median P/B Ratio was $-1.07 standard deviation P/B Ratio was $13.97 minimum P/B Ratio was $-76.12 maximum P/B Ratio was $129.06
# function to create labeled barplots
def labeled_barplot(df, feature, perc=False, n=None, figsize=None):
"""
Barplot with percentage at the top
data: dataframe
feature: dataframe column
perc: whether to display percentages instead of count (default is False)
n: displays the top n category levels (default is None, i.e., display all levels)
"""
total = len(df[feature]) # length of the column
count = df[feature].nunique()
if figsize==None:
if n is None:
plt.figure(figsize=(count + 1, 5))
else:
plt.figure(figsize=(n + 1, 5))
else:
plt.figure(figsize=figsize)
plt.xticks(rotation=90, fontsize=14)
ax = sns.countplot(
data=df,
x=feature,
palette="Paired",
order=df[feature].value_counts().index[:n].sort_values(),
)
for p in ax.patches:
if perc == True:
label = "{:.1f}%".format(
100 * p.get_height() / total
) # percentage of each class of the category
else:
label = p.get_height() # count of each level of the category
x = p.get_x() + p.get_width() / 2 # width of the plot
y = p.get_height() # height of the plot
ax.annotate(
label,
(x, y),
ha="center",
va="center",
size=12,
xytext=(0, 5),
textcoords="offset points",
) # annotate the percentage
plt.show() # show the plot
GICS Sector¶labeled_barplot(df, 'GICS Sector', perc=True, figsize=(7,1))
The Industrials Sector had the most companies in the sample of 340 companies.
GICS Sub-Industry Issue: Convert Series to DataFrame and Get output of Index values:¶Not working / deprecated: unpack(df) How to unpack the columns of a pandas DataFrame to multiple variables
Issue: Works on DataFrame not Series: DataFrame.unstack(level=- 1, fill_value=None) pandas.DataFrame.unstack
Solution: df['GICS Sector'].apply(pd.Series) | out.apply(pd.Series) How to unpack a Series of tuples in Pandas?
type(df['GICS Sector'].value_counts().index)
# how to unpack pandas index
pandas.core.indexes.base.Index
df['GICS Sector'].apply(pd.Series).head(3)
| 0 | |
|---|---|
| 0 | Industrials |
| 1 | Health Care |
| 2 | Health Care |
GICS_Sector_Series_as_df=df['GICS Sector'].apply(pd.Series)
GICS_Sector_Series_as_df.keys()
RangeIndex(start=0, stop=1, step=1)
type(GICS_Sector_Series_as_df)
pandas.core.frame.DataFrame
GICS_Sector_Series_as_df.keys() # RangeIndex(start=0, stop=1, step=1)
type(GICS_Sector_Series_as_df.keys())
GICS_Sector_Series_as_df.keys()
RangeIndex(start=0, stop=1, step=1)
print(GICS_Sector_Series_as_df.keys())
RangeIndex(start=0, stop=1, step=1)
unused: Pandas DataFrame RangeIndex
From ChatGPT3: How to see the keys?:
print(GICS_Sector_Series_as_df.columns)
RangeIndex(start=0, stop=1, step=1)
print(GICS_Sector_Series_as_df.columns)
RangeIndex(start=0, stop=1, step=1)
print(GICS_Sector_Series_as_df.keys().tolist())
[0]
GICS_Sector_Series_as_df.head(3)
| 0 | |
|---|---|
| 0 | Industrials |
| 1 | Health Care |
| 2 | Health Care |
type(GICS_Sector_Series_as_df[0])
pandas.core.series.Series
GICS_Sector_Series_as_df.index
RangeIndex(start=0, stop=340, step=1)
GICS Sub-Industry Issue: Convert Series to DataFrame and Get output of Index values:¶GICS_Sector_Series_as_df.index.tolist()[:8] # 0 to 7 of all 340 index values
[0, 1, 2, 3, 4, 5, 6, 7]
len(GICS_Sector_Series_as_df.index.tolist()) # length or number of values in this dataframe from Series index is list of 340 values
340
DataFrame.sort_values(
by,
*,
axis=0,
ascending=True,
inplace=False,
kind='quicksort',
na_position='last',
ignore_index=False,
key=None)
Sort by the values along either axis. (axis=0 or 1)
by :
str or list of str
Name or list of names to sort by.
- if axis is 0 or ‘index’ then by may contain index levels and/or column labels.
- if axis is 1 or ‘columns’ then by may contain column levels and/or index labels.
axis :
{0 or ‘index’, 1 or ‘columns’}, default 0
Axis to be sorted.
ascending :
bool or list of bool, default True
Sort ascending vs. descending. Specify list for multiple sort orders. If this is a list of bools, must match the length of the by.
inplace :
bool, default False
If True, perform operation in-place.
kind :
{‘quicksort’, ‘mergesort’, ‘heapsort’, ‘stable’}, default ‘quicksort’
Choice of sorting algorithm. See also numpy.sort() for more information.
mergesort and stable are the only stable algorithms.
For DataFrames, this option is only applied when sorting on a single column or label.
na_position :
{‘first’, ‘last’}, default ‘last’
Puts NaNs at the beginning if first; last puts NaNs at the end.
ignore_index :
bool, default False
If True, the resulting axis will be labeled 0, 1, …, n - 1.
na_position :
{‘first’, ‘last’}, default ‘last’
Puts NaNs at the beginning if first; last puts NaNs at the end.
ignore_index :
bool, default False
If True, the resulting axis will be labeled 0, 1, …, n - 1.
key :
callable, optional
Apply the key function to the values before sorting. This is similar to the key argument in the builtin sorted() function, with the notable difference that this key function should be vectorized. It should expect a Series and return a Series with the same shape as the input. It will be applied to each column in by independently.
Returns
DataFrame or None
DataFrame with sorted values or None if inplace=True.
See also
DataFrame.sort_index
Sort a DataFrame by the index.
Series.sort_values
Similar method for a Series.
GICS Sub Industry¶sub_ind_order_counts=df['GICS Sub Industry'].sort_values(inplace=False).value_counts()
sub_ind_order_counts.head(4)
GICS Sub Industry Oil & Gas Exploration & Production 16 REITs 14 Industrial Conglomerates 14 Internet Software & Services 12 Name: count, dtype: int64
df_GICS=df.copy()
# sub_ind_order_counts=df['GICS Sub Industry'].sort_values(inplace=False).value_counts()
df_GICS_sorted=df_GICS['GICS Sub Industry'].sort_values().value_counts()
df_GICS.sort_values(by=['GICS Sub Industry'],inplace=True)
df_GICS_sorted.head(3)
GICS Sub Industry Oil & Gas Exploration & Production 16 REITs 14 Industrial Conglomerates 14 Name: count, dtype: int64
type(df_GICS_sorted)
pandas.core.series.Series
References:
Series.to_frame(name=_NoDefault.no_default) pandas.Series.to_frame# Note1: convert pandas Series to DataFrame using above reference 2.
# operates on df_GICS_sorted Series
# which is a sorted copy of df
# sorted for the purpose of getting a sorted plot when fed into
# labeled_barplot plotting function
df_GICS_sorted.to_frame().head(4)
| count | |
|---|---|
| GICS Sub Industry | |
| Oil & Gas Exploration & Production | 16 |
| REITs | 14 |
| Industrial Conglomerates | 14 |
| Internet Software & Services | 12 |
df_GICS_sorted.head(3)
GICS Sub Industry Oil & Gas Exploration & Production 16 REITs 14 Industrial Conglomerates 14 Name: count, dtype: int64
df_GICS_sorted_to_df=df_GICS_sorted.to_frame()
type(df_GICS_sorted)
pandas.core.series.Series
type(df_GICS_sorted_to_df)
pandas.core.frame.DataFrame
labeled_barplot(df_GICS_sorted_to_df.sort_values(by=['count']), feature='count', perc=True, figsize=(9,1))
df.sample(1)
| Ticker Symbol | Security | GICS Sector | GICS Sub Industry | Current Price | Price Change | Volatility | ROE | Cash Ratio | Net Cash Flow | Net Income | Earnings Per Share | Estimated Shares Outstanding | P/E Ratio | P/B Ratio | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 132 | FLR | Fluor Corp. | Industrials | Diversified Commercial Services | 47.220001 | 10.819056 | 1.774454 | 14 | 73 | -43239000 | 412512000 | 2.85 | 144741052.6 | 16.568421 | 14.992623 |
df_3=df.copy()
df_3.head(1) # DataFrame
| Ticker Symbol | Security | GICS Sector | GICS Sub Industry | Current Price | Price Change | Volatility | ROE | Cash Ratio | Net Cash Flow | Net Income | Earnings Per Share | Estimated Shares Outstanding | P/E Ratio | P/B Ratio | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | AAL | American Airlines Group | Industrials | Airlines | 42.349998 | 9.999995 | 1.687151 | 135 | 51 | -604000000 | 7610000000 | 11.39 | 668129938.5 | 3.718174 | -8.784219 |
Reference: seaborn.countplot
lookup_table=df_3['GICS Sub Industry'].value_counts()
lookup_table.head(4)
GICS Sub Industry Oil & Gas Exploration & Production 16 REITs 14 Industrial Conglomerates 14 Electric Utilities 12 Name: count, dtype: int64
# from def labeled_barplot(df, feature, perc=False, n=None)
# use:
# sns.countplot
# plt.show()
def labeled_barplot_2(df, feature, perc=False, n=None):
total = len(df[feature]) # length of the column
count = df[feature].nunique()
if n is None:
plt.figure(figsize=(count + 1, 5))
else:
plt.figure(figsize=(n + 1, 5))
sns.countplot(data=df_3, x='GICS Sub Industry', palette="Paired", order=df_3['GICS Sub Industry'].value_counts().index)
plt.xticks(rotation=90, fontsize=10)
ax = sns.countplot(
data=df,
x=feature,
palette="Paired",
order=df[feature].value_counts().index[:n].sort_values(),
)
for p in ax.patches:
if perc == True:
label = "{:.1f}%".format(
100 * p.get_height() / total
) # percentage of each class of the category
else:
label = p.get_height() # count of each level of the category
x = p.get_x() + p.get_width() / 2 # width of the plot
y = p.get_height() # height of the plot
ax.annotate(
label,
(x, y),
ha="center",
va="center",
size=12,
xytext=(0, 5),
textcoords="offset points",
) # annotate the percentage
plt.show()
want to use labeled_barplot_2() function to plot a barplot, whereby the lookup_table is consulted to get the order of the bars in the barplot [Deprecated]
TabNine and GitHub Copilot assisted in figuring out how to order labeled barplot bars by count of companies in each 'GCIS Sub Industry' Subsector.
df_3['GICS Sub Industry'].value_counts().index[:10].sort_values()
Index(['Banks', 'Diversified Financial Services', 'Electric Utilities',
'Health Care Equipment', 'Industrial Conglomerates',
'Internet Software & Services', 'MultiUtilities',
'Oil & Gas Exploration & Production', 'Property & Casualty Insurance',
'REITs'],
dtype='object', name='GICS Sub Industry')
df_3['GICS Sub Industry']
0 Airlines
1 Pharmaceuticals
2 Health Care Equipment
3 Application Software
4 Semiconductors
...
335 Internet Software & Services
336 Restaurants
337 Health Care Equipment
338 Regional Banks
339 Pharmaceuticals
Name: GICS Sub Industry, Length: 340, dtype: object
labeled_barplot(df, 'GICS Sub Industry', perc=True) ## Complete the code to create a labelled barplot for 'GICS Sub Industry'
type(sub_ind_order_counts)
pandas.core.series.Series
Q/ I was wondering om python: how to use sns.countplot on df to plot in descending order based not on a feature directly of the df but on counts of unque value in that feature
A/ You can use the value_counts() method in Pandas to count the number of occurrences of each unique value in the column of interest, and then use that to sort the order of the bars in your Seaborn countplot.
Here's an example of how to do it:
df = pd.DataFrame({
'fruit': ['apple', 'banana', 'apple', 'orange', 'banana', 'banana', 'kiwi']
})
# Count the number of occurrences of each unique value in the 'fruit' column
fruit_counts = df['fruit'].value_counts()
# Sort the order of the bars in the countplot based on the counts of each unique value
sns.countplot(x='fruit', data=df, order=fruit_counts.index)
step 0: labeled_barplot will be modified so that it can receive a parameter 'sort_by_count' to change the way the countplot is ordered
step 2: The modified function labeled_barplot based on sns.countplot is as follows:
def labeled_barplot_6(df, feature, perc=False, n=None, sort_by_count=False):
"""
Barplot with percentage at the top
data: dataframe
feature: dataframe column
perc: whether to display percentages instead of count (default is False)
n: displays the top n category levels (default is None, i.e., display all levels)
sort_by_count: whether to sort the categories by count (default is False)
"""
total = len(df[feature]) # length of the column
count = df[feature].nunique()
if n is None:
plt.figure(figsize=(count + 1, 5))
else:
plt.figure(figsize=(n + 1, 5))
plt.xticks(rotation=90, fontsize=15)
if sort_by_count:
ax = sns.countplot(
data=df,
x=feature,
palette="Paired",
order=df[feature].value_counts().sort_values(ascending=False).index[:n],
)
else:
ax = sns.countplot(
data=df,
x=feature,
palette="Paired",
order=df[feature].sort_values().unique()[:n],
)
for p in ax.patches:
if perc == True:
label = "{:.1f}%".format(
100 * p.get_height() / total
) # percentage of each class of the category
labeled_barplot_6(df, 'GICS Sub Industry', perc=True, n=None, sort_by_count=True)
# where n is the number of sub sectors to display on the countplot
# df.info()
df["Security"].head(1)
0 American Airlines Group Name: Security, dtype: object
df_nameless = df.drop(["Security", "Ticker Symbol"], axis=1)
The following steps are adapted from chatgpt3 (Up to (including) create heat map of the reordered pivot table):
Create a pivot table, with indexes for 2 naming columns Ticker Name and Security.
chatgpt3: use pairplot and scatterplot:
estimated time for sns.pairplot: ~26.1 - 32.8 s
sns.pairplot(df)
<seaborn.axisgrid.PairGrid at 0x2cda3023070>
pivot = df.pivot_table(values='Value', index=['Name1', 'Name2'], columns=['Category1', 'Category2'], fill_value=0)
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 340 entries, 0 to 339 Data columns (total 15 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Ticker Symbol 340 non-null object 1 Security 340 non-null object 2 GICS Sector 340 non-null object 3 GICS Sub Industry 340 non-null object 4 Current Price 340 non-null float64 5 Price Change 340 non-null float64 6 Volatility 340 non-null float64 7 ROE 340 non-null int64 8 Cash Ratio 340 non-null int64 9 Net Cash Flow 340 non-null int64 10 Net Income 340 non-null int64 11 Earnings Per Share 340 non-null float64 12 Estimated Shares Outstanding 340 non-null float64 13 P/E Ratio 340 non-null float64 14 P/B Ratio 340 non-null float64 dtypes: float64(7), int64(4), object(4) memory usage: 40.0+ KB
sel_cols = [c for c in df.columns if df[c].dtype in ['object', 'datetime64[ns]']]
sparkbyexample | select columns by type
# list comprehensions per chatgpt3
idx_cols=[c for c in df.columns if df[c].dtype in ['object']] # GitHub Copilot
valu_cols=[c for c in df.columns if df[c].dtype in ['float64', 'int64']] # GitHub Copilot, Tabnine AI
pivot = df.pivot_table(values=valu_cols, index=idx_cols, aggfunc='mean') # GitHub Copilot, Tabnine AI
row_dist = pd.DataFrame(squareform(pdist(pivot, metric='euclidean')), columns=pivot.index, index=pivot.index)
# NameError: name 'squareform' is not defined # ChatgGPT3
# from scipy.spatial.distance import pdist, squareform
row_dist=pd.DataFrame(squareform(pdist(pivot, metric='euclidean')), columns=pivot.index, index=pivot.index) # GitHub Copilot, Tabnine AI, ChatGPT3
row_clusters = linkage(row_dist, method='complete')
row_clusters = linkage(row_dist, method='complete')
Create the dendrogram of the clusters:
fig = plt.figure(figsize=(8,8))
axd = fig.add_axes([0.09,0.1,0.2,0.6])
row_dendr = dendrogram(row_clusters, orientation='left')
fig = plt.figure(figsize=(8,8))
axd = fig.add_axes([0.09,0.1,0.2,0.6])
row_dendr = dendrogram(row_clusters, orientation='left')
df_rowclust = pivot.iloc[row_dendr['leaves']]
df_rowclust = pivot.iloc[row_dendr['leaves']]
sns.heatmap(df_rowclust, cmap='RdBu_r')
plt.figure(figsize=(9, 5))
sns.heatmap(
df_rowclust.corr(), annot=True, vmin=-1, vmax=1, fmt=".2f", cmap="Spectral"
)
plt.show()
From the above analysis, in which a heatmap was created from a dataframe which was created from reordering Ticker Name, Security, GICS Sector, and GICS Sub Industry: (GitHub Copilot assisted.)
Estimated Shares Outstanding and P/E Ratio are not highly correlated. (although GitHub Copilot suggested they would be.)
("Highly" is used relatiely here, as the correlations are not extremely high for any variable pair.)
Net Income is highly correlated to Earnings Per Share, and Estimated Shares Outstanding. (56%, 59%, respectively)
Earnings per share is also highly correlated to Current Price. (48%)
Puzzlingly, from this heatmap: if you get good Earnings per Share you would expect a poor Return on Equity (ROE) ( -41%).
note: Excel file heatmap_insights_anaysis.xlsx must be open for load() from xlwings library to run
# loads from excel using xlwings
# load()
pd.read_excel('../data/heatmap_insights_analysis.xlsx')
| case | profitable | reinvesting profits into growth | paying dividend | case.1 | issue | |
|---|---|---|---|---|---|---|
| 0 | 1 | y | y | y/n | 1 | NaN |
| 1 | 2 | n | y | n | 2 | no profits to reinvest in growth |
| 2 | 3 | n | y | y | 3 | don't dividends get paid out of profits? (Also... |
| 3 | 4 | y | y | y | 4 | NaN |
| 4 | 5 | y | y | n | 5 | NaN |
| 5 | 6 | y | n | y | 6 | NaN |
| 6 | 7 | y | n | n | 7 | NaN |
| 7 | 8 | NaN | NaN | NaN | 8 | NaN |
Possibly, gains are already priced into stocks with good Earnings per Share,
so that the stock price has already peaked, reducing likelihood of Return on Equity in this case of high Earnings per Share. (This line with support from GitHub Copilot.)
(If a company had good profits (Earnings) (last quarter) and low stock price (GH Copilot), Earnings per Share is high (high EPS).)
However, as the quarter progresses there may be an unknown deviation of actual from expected earnings per share,
which only becomes apparent to the market when Earnings are reported
so that EPS and Stock Price are likely to change as soon as Earnings come in (earnings season).
When is earnings Season 2023 US? Investopedia | Earnings Season
Earnings season
typically begin
in the month following
most major companies' fiscal quarters:
January, April, July, and October.
It generally lasts about 6 weeks,
at which point the number of earnings reports being released
return to non-earnings season levels.
To return to the subject of trying to explain the inverse correlation between ROE and EPS, GitHub Copilot suggested that this was a function of reinvestment of profits and paying a dividend. However, since both of these would appear to be dependent on being profitable, more certainly over an extended period of time, some of the answers given seem to be contradictory.
One explanation that seems to make sense is that as soon as a company publishes earnings (profits), these are factored into the price, and therefore earnings per share is no longer a good indicator of future price, but instead is a lagging indicator (rather than a leading indicator).
According to geckoboard.com:
Lagging indicators take a long time to change, and show the later-stage results of your efforts. Leading indicators, on the other hand, measure the activities you think will help you reach your goal, and can be tracked on a more ongoing basis.
However, we will try for another explanation of leading and lagging indicator.
Economists and investors are constantly watching for:
The most closely watched of these signs are:
All indicators fall into 1 of 3 categories:
From the above definition of Lagging indicators from investopedia.com, the EPS (Earnings per Share) would seem to indicate a pattern that is retrospective and hence a lagging indicator.
The company carries out business operations:
(A summary of key performance indicators and information on the business are published in the 10-K Yearly Shareholder report, publicly available on SEC.gov for all publicly traded companies in the United States.)
For example, here is an adapted excerpt (on Competition) from the APPL 2022 10-K Report, which distills the factors which characterize the market environment in which business operations take place:
The markets for the Company’s products and services are
- highly competitive, and
- are characterized by:
1. aggressive price competition
(- and resulting downward pressure on:
- gross margins),
2. frequent introduction of new products and services,
3. short product life cycles,
4. evolving industry standards,
5. continual improvement in product price and performance characteristics,
6. rapid adoption of technological advancements by competitors, and
7. price sensitivity on the part of consumers and businesses.
(Side note: An argument (not discussed here) could also be made that price has an element of speculation built in, whether this is a leading indicator is another debate.)
In summary of thoughts to date: there are 2 mechanisms at work:
Therefore, primarily, Earnings Per Share (EPS) is not a leading indicator, and therefore cannot be used as a predictor for future Return on Equity (ROE).
Secondarily, due to the pursuit of the "buy low, sell high" ideal by retail traders
recent conditions have led to newly proposed: Bonds Over Stocks: The New 60-40 Portfolio
Why a 60/40 Portfolio Is No Longer Good Enough | investopedia.com
A series of bear markets that started in 2000 coupled with historically low-interest rates have eroded the popularity of [portfolios made by financial planners and stockbrokers for their clients composed of 60% equities and 40% bonds].
To summarize thoughts on negative correlation between EPS and ROE:
Of note:
not everyone 'buys into' technical analysis (2)
Technical analysis may be contrasted with fundamental analysis, which focuses on a company's financials rather than historical price patterns or stock trends.
Some opt for fundamental analysis
measures a security's intrinsic value by examining related economic and financial factors.end goal is to determine a number that an investor can compare with a security's current price to see whether the security isundervalued orovervaluedby other investors. (3)Continuining on both the themes of:
determine a number that an investor can compare with a security's current price to see whether the security is - `undervalued or`<br>
- `overvalued` (1)
(1) fundamental analysis | Investopedia
...
If instead of buying low and selling high retail traders are more likely to buy high and sell low (particularly in the tract of market history analysed here), then it may indicate that either a good analysis or understanding of the fundamentals is not occurring or that despite best efforts emotion is taking over causing mass buying of overheated hyped stock bubbles under the mistaken forecast, say, that upward trends will continue as they are about to burst.
This would in effect lead to the inverted:
buy high sell low.
If this is in fact what is occurring over this period,
this would be reflected in high earning per share companies
with good quarterly and yearly reports and businesses
in all or certain areas of the market which may be overheated
being overbought,
so that they have already been overbought,
leading to fewer shares outstanding,
creating upward price pressure, due to Keynsian scarcety of supply of the stock,
crashing through resistance bands being supported by the rising tide of buyers watching the price rise and trying to get in on the bull run,
only to be stopped out
of trades when the bubble bursts and buyers loose interest,
leading to
high earnings per share stocks being overbought initally (at high prices) during the up trend,
only to be then oversold in the subsequent correction (possible overcorrection),
(depending on what the coefficient of damping for the economic system being modelled is)
and whether there is over and undershoot or not of the theoretical real price of the stock.
The stock underperformed when compared to some of its competitors Wednesday, as Tesla Inc. TSLA, -7.77% fell 2.02% to $180.59.
Trading volume (68.9 M) eclipsed its 50-day average volume of 68.5 M.
(1)
(1) Ford Motor Co. stock falls Wednesday, underperforms market | MarketWatch
[April 20, 2023 10:45 AM]
Monday marks the last chance for investors to receive the next dividend payout from Ford Motor
The company announced on Thursday that it would pay shareholders a quarterly dividend of 15 cents per share. On Tuesday, Ford Motor will go ex-dividend, meaning the stock will trade lower to reflect that payout. In other words, the stock will likely open 15 cents lower than it would have opened on any other day.
measures the market's valuation of a company relative to its book value.
to compare a firm's market capitalization to its book value and locate undervalued companies
ratio is calculated by dividing the company's current stock price per share by its book value per share (BVPS)
P/B Ratio= Book Value per Share (BVPS) / Market Price per Share
(1)
(1) Price-to-Book (PB) Ratio: Meadning, Formula, and Example
The following is an adapted excerpt from What Are Intangible Assets? Examples and How to Value:
Goodwill, brand recognition and intellectual property, such as patents, trademarks, and copyrights, are all intangible assets.
Intangible assets exist in opposition to tangible assets,
Additionally, financial assets such as:
(1)
Let's check the stocks of which economic sector have seen the maximum price increase on average.
plt.figure(figsize=(6, 1.5))
sns.barplot(data=df, x='GICS Sector', y='Price Change', ci=False) ## Complete the code to choose the right variables
plt.xticks(rotation=90)
plt.show()
plt.figure(figsize=(5,1.2))
sns.barplot(data=df, x='GICS Sector', y='Cash Ratio', ci=False) ## Complete the code to choose the right variables
plt.xticks(rotation=90)
plt.show()
assets that can be traded, or they can also be seen as packages of capital that may be traded.
Capital is a broad term that can describe anything that:
While money itself may be construed as capital,
An investor is any person or other entity (such as a firm or mutual fund)
Investors rely on different financial instruments
A financial asset is a:
liquid asset
bank deposits are all are examples of financial assets.
Unlike:
tangible physical assets,
financial assets do not necessarily have inherent:
physical form.
Rather, their value reflects factors of supply and demand
An asset is a
P/E ratios can help determine the relative value of a company's shares as they signify the amount of money an investor is willing to invest in a single share of a company per dollar of its earnings. Let's see how the P/E ratio varies, on average, across economic sectors.
plt.figure(figsize=(5,1))
sns.barplot(data=df, x='GICS Sector', y='P/E Ratio', ci=False) ## Complete the code to choose the right variables
plt.xticks(rotation=90)
plt.show()
P/E Ratio | INVESTING FUNDAMENTAL ANALYSIS
The price-to-earnings ratio is the ratio for valuing a company that measures its current share price relative to its earnings per share (EPS).
The price-to-earnings ratio is also sometimes known as the:
P/E ratios are used by:
in an apples-to-apples comparison.
It can also be used to compare:
P/E may be estimated on a:
potentially:
Companies that have no earnings or that are losing money do not have a P/E ratio because there is nothing to put in the denominator.
Two kinds of P/E ratios—forward and trailing P/E—are used in practice.
A P/E ratio holds:
when compared against:
same industry
or
P/E Ratio= Earnings per share / Market value per share
To determine the P/E value, one must simply divide the current stock price by the earnings per share (EPS), that is:
P/E ratio = current stock price / earnings per share (EPS)
The current stock price (P) can be found simply by plugging a stock’s ticker symbol into any finance website, and although this concrete value reflects what investors must currently pay for a stock, the EPS is a slightly more nebulous figure.
EPS comes in two main varieties. TTM is a Wall Street acronym for "trailing 12 months". This number signals the company's performance over the past 12 months. The second type of EPS is found in a company's earnings release, which often provides EPS guidance. This is the company's best-educated guess of what it expects to earn in the future. These different versions of EPS form the basis of trailing and forward P/E, respectively.
The P/E ratio helps one determine whether a stock is overvalued or undervalued. A company's P/E can also be benchmarked against other stocks in the same industry or against the broader market, such as the S&P 500 Index.
Sometimes, analysts are interested in long-term valuation trends and consider the P/E 10 or P/E 30 measures, which average the past 10 or past 30 years of earnings, respectively. These measures are often used when trying to gauge the overall value of a stock index, such as the S&P 500, because these longer-term measures can compensate for changes in the business cycle.
The P/E ratio of the S&P 500 has fluctuated from a low of around 5x (in 1917) to over 120x (in 2009 right before the financial crisis). The long-term average P/E for the S&P 500 is around 16x, meaning that the stocks that make up the index collectively command a premium 16 times greater than their weighted average earnings.
Analysts and investors review a company's P/E ratio when they determine if the share price accurately represents the projected earnings per share.
Some investors prefer to look at the trailing P/E because they don't trust another individual’s earnings estimates. But the trailing P/E also has its share of shortcomings—namely, that a company’s past performance doesn’t signal future behavior.
Investors should thus commit money based on future earnings power, not the past.
Earnings power is a figure that telegraphs a business's
Earnings power factors in several elements, including a company’s:
Earning power likewise considers metrics such as a company's:
Furthermore, some companies determine earnings power based on:
A company can cultivate a keen insight into its earnings power by examining earnings before interest and tax (EBIT).
By generally excluding any and all irregular income or expenses,
(1)
(1) CORPORATE FINANCE > CORPORATE FINANCE BASICS > Earnings Power | Investopedia
The following excerpt is adapted from CORPORATE FINANCE > FINANCIAL STATEMENTS > Earnings Before Interest and Taxes (EBIT) | Investopedia
Earnings before interest and taxes (EBIT) is an indicator of a company's profitability.
EBIT can be calculated as revenue minus expenses excluding tax and interest.
EBIT is also referred to as:
CORPORATE FINANCE > FINANCIAL STATEMENTS > Earnings Before Interest and Taxes (EBIT) | Investopedia
Revenue is the money generated from normal business operations, calculated as the average sales price times the number of units sold.
Revenue = (average sales price) * (number of units sold)
top line (or gross income) figure from which costs are subtracted to determine net income.
Revenue is also known as sales on the income statement.
CORPORATE FINANCE > FINANCIAL STATEMENTS > Revenue | Investopedia
The other two key statements are:
the balance sheet and
the cash flow statement.
The following excerpt is adapted from: CORPORATE FINANCE > FINANCIAL STATEMENTS > Financial Statements | Investopedia
Financial statements convey business activities and financial performance of a company.
Financial statements are often audited to ensure accuracy.
Primary financial statements include:
statement of changes in equity.
balance sheet: provides an overview of assets, liabilities, and shareholders' equity as a snapshot in time.
Investors and financial analysts rely on financial data to analyze the performance of a company and
One of the most important resources of reliable and audited financial data is the annual report, which contains the firm's financial statements.
The 3 major financial statement reports are:
The balance sheet
The date at the top of the balance sheet tells you:
Below is a breakdown of the items in a balance sheet.
Trademarks, patents, goodwill, and other intangible assets can't physically be touched but have future economic (and often long-term benefits) for the company.
Shareholders' equity is a company's total assets minus its total liabilities. Shareholders' equity (also known as stockholders' equity) represents the amount of money that would be returned to shareholders if all of the assets were liquidated and all of the company's debt was paid off. Retained earnings are part of shareholders' equity and are the amount of net earnings that were not paid to shareholders as dividends.
Below is a portion of ExxonMobil Corporation's (XOM) balance sheet for fiscal year 2021, reported as of Dec. 31, 2021.
Total assets: $338.9 billion.
Total liabilities: $163.2 billion.
Total equity: $175.7 billion.
Total liabilities + equity = $338.9 billion = total assets for the period.
Investor Relations | Earnings Call | Exxonmobil
see next cell for xlwings library method load() from excel sheet with same Balance sheet referenced in (1) but taken from (2) for ease of formatting (2)
(1)
(1) CORPORATE FINANCE > FINANCIAL STATEMENTS > Financial Statements | Investopedia
(2) 2021, UNITED STATES, SECURITIES AND EXCHANGE COMMISSION, WASHINGTON, D.C. 20549 | FORM 10-K ☑ ANNUAL REPORT PURSUANT TO SECTION 13 OR 15(d) OF THE SECURITIES EXCHANGE ACT OF 1934 | For the fiscal year ended December 31, 2021 | Exxon Mobil Corporation (see page 72 for Balance Sheet on December 31, 2021)
Note: from earlier: these values are in millions of USD.
Exxon2021=pd.read_excel('../data/Balance-Sheet-Exxon-Mobil-2021.xlsx')Exxon2021# rerun pd.read_excel() and save to variable
Exxon2021=pd.read_excel('../data/Balance-Sheet-Exxon-Mobil-2021.xlsx')
Exxon2021
| Elements | Reference | December 31, 2021 | December 31, 2020 | |
|---|---|---|---|---|
| 0 | Assets | NaN | NaN | NaN |
| 1 | Current assets | NaN | NaN | NaN |
| 2 | Cash and cash equivalents | NaN | 6,802 | 4,364 |
| 3 | Notes and accounts receivable - net | 6.0 | 32,383 | 20,581 |
| 4 | Inventories | NaN | NaN | NaN |
| 5 | Crude oil, products and merchandise | 3.0 | 14,519 | 14,169 |
| 6 | Materials and supplies | NaN | 4,261 | 4,681 |
| 7 | Other current assets | NaN | 1,189 | 1,098 |
| 8 | Total current assets | NaN | 59,154 | 44,893 |
| 9 | Investments, advances and long-term receivables | 8.0 | 45,195 | 43,515 |
| 10 | Property, plant and equipment, at cost, less a... | 9.0 | 216,552 | 227,553 |
| 11 | Other assets, including intangibles - net | NaN | 18,022 | 16,789 |
| 12 | Total assets | NaN | 338,923 | 332,750 |
| 13 | NaN | NaN | NaN | NaN |
| 14 | Liabilities | NaN | NaN | NaN |
| 15 | Current liabilities | NaN | NaN | NaN |
| 16 | Notes and loans payable | 6.0 | 4,276 | 20,458 |
| 17 | Accounts payable and accrued liabilities | 6.0 | 50,766 | 35,221 |
| 18 | Income taxes payable | NaN | 1,601 | 684 |
| 19 | Total current liabilities | NaN | 56,643 | 56,363 |
| 20 | Long-term debt | 14.0 | 43,428 | 47,182 |
| 21 | Postretirement benefits reserves | 17.0 | 18,430 | 22,415 |
| 22 | Deferred income tax liabilities | 19.0 | 20,165 | 18,165 |
| 23 | Long-term obligations to equity companies | NaN | 2,857 | 3,253 |
| 24 | Other long-term obligations | NaN | 21,717 | 21,242 |
| 25 | Total liabilities | NaN | 163,240 | 168,620 |
| 26 | NaN | NaN | NaN | NaN |
| 27 | Commitments and contingencies | 16.0 | NaN | NaN |
| 28 | NaN | NaN | NaN | NaN |
| 29 | Equity | NaN | NaN | NaN |
| 30 | Common stock without par value | NaN | 15,746 | 15,688 |
| 31 | (9,000 million shares authorized, 8,019 millio... | NaN | NaN | NaN |
| 32 | Earnings reinvested | NaN | 392,059 | 383,943 |
| 33 | Accumulated other comprehensive income | NaN | -13764 | -16705 |
| 34 | Common stock held in treasury | NaN | -225464 | -225776 |
| 35 | (3,780 million shares in 2021 and 3,786 millio... | NaN | NaN | NaN |
| 36 | ExxonMobil share of equity | NaN | 168,577 | 157,150 |
| 37 | Noncontrolling interests | NaN | 7,106 | 6,980 |
| 38 | Total equity | NaN | 175,683 | 164,130 |
| 39 | Total liabilities and equity | NaN | 338,923 | 332,750 |
| 40 | NaN | NaN | NaN | NaN |
| 41 | The information in the Notes to Consolidated F... | NaN | NaN | NaN |
| 42 | 72 | NaN | NaN | NaN |
Volatility accounts for the fluctuation in the stock price. A stock with high volatility will witness sharper price changes, making it a riskier investment. Let's see how volatility varies, on average, across economic sectors.
Volatility is a measure of distribution of returns for a security or index. Higher volatility = riskier security
- Volatility is measured from:
- the standard deviation or variance in returns from the security or index.
In the securities markets, volatility is often associated with big swings in either direction.
e.g.
- when the stock market rises and falls more than one percent over a sustained period of time, it is called a volatile market.
- An asset's volatility is a key factor when pricing options contracts.
OPTIONS AND DERIVATIVES > STRATEGY & EDUCATION > Volatility | Investopedia
A "security" is a financial instrument with monetary value.
- A security can represent:
- owning a corporation (stock),
- a creditor relationship with a government body / corporation (bond);
- or rights to ownership (option).
INVESTING > INVESTING BASICS > Financial Securities | Investopedia
Call options:
- financial contracts that give the option buyer the right but not the obligation to buy a:
- stock,
- bond,
- commodity or
- other asset / instrument
at a price, within a time period.
The stock, bond, or commodity is the underlying asset.
A call buyer profits when the underlying asset increases in price.
TRADING > OPTIONS AND DERIVATIVES > Call Option | Investopedia
Underlying asset are the financial assets upon which a derivative’s price is based.
Options are an example of a derivative.
A derivative is a financial instrument with a price that is based on a different asset.
Derivative :
- a type of financial contract whose value is dependent on an underlying asset, group of assets, or benchmark.
- set between 2 or more parties that can trade on an exchange or over-the-counter (OTC).
TRADING > OPTIONS AND DERIVATIVES > Derivative | Investopedia
Over-the-counter (OTC)
- the process of trading securities via a broker-dealer network as opposed to on a centralized exchange like the New York Stock Exchange.
Over-the-counter trading can involve:
- stocks,
- bonds, and
- derivatives,
- which are financial contracts that derive their value from an underlying asset such as a commodity.
STOCK TRADING > STOCK TRADING STRATEGY & EDUCATION > Over-the-Counter | Investopedia
A commodity is a basic good used in commerce that is interchangeable with other goods of the same type. Commodities are most often used as inputs in the production of other goods or services. A commodity thus usually refers to a raw material used to manufacture finished goods. A product, on the other hand, is the finished good sold to consumers.
Commodity and Product
- (Often confused, used interchangeably)
- different when used by traders today.
Commodity - a raw material used to manufacture finished goods
Product - the finished good sold to consumers
Commodities and products are part of the production and manufacturing process.
The main difference is where they are in the chain.
Commodities are typically in the early stages of production.
Products fall at the final stage.
Raw materials
- materials / substances used in the primary production or manufacturing of goods.
- commodities bought and sold on commodities exchanges worldwide.
- Businesses buy and sell raw materials in the factor market because raw materials are factors of production.
"Factor market"
- a term economists use for all resources businesses use to purchase, rent, or hire what they need to produce goods / services.
- Those needs are the factors of production, which include raw materials, land, labor, and capital.
The factor market is also called the input market.
- By this definition, all markets are either factor markets, where businesses obtain the resources they need,
- or goods and services markets, where consumers make their purchases.
Factors of production:
- inputs needed for creating a good or service
- include:
- land,
- labor,
- entrepreneurship, and
- capital.
Those who control the factors of production often enjoy the greatest wealth in a society. In capitalism, the factors of production are most often controlled by business owners and investors. In socialist systems, the government (or community) often exerts greater control over the factors of production.
CORPORATE FINANCE > ACCOUNTING > Factors of Production | Investopedia
An entrepreneur is an individual who creates a new business, bearing most of the risks and enjoying most of the rewards. The process of setting up a business is known as entrepreneurship. The entrepreneur is commonly seen as an innovator, a source of new ideas, goods, services, and business/or procedures.
Entrepreneurs play a key role in any economy, using the skills and initiative necessary to anticipate needs and bring good new ideas to market. Entrepreneurship that proves to be successful in taking on the risks of creating a startup is rewarded with profits, fame, and continued growth opportunities. Entrepreneurship that fails results in losses and less prevalence in the markets for those involved.
The term startup refers to a company in the first stages of operations. Startups are founded by one or more entrepreneurs who want to develop a product or service for which they believe there is demand. These companies generally start with high costs and limited revenue, which is why they look for capital from a variety of sources such as venture capitalists.
A venture capitalist (VC) is a private equity investor that provides capital to companies with high growth potential in exchange for an equity stake. A VC investment could involve funding startup ventures or supporting small companies that wish to expand but have no access to the equities markets.
ALTERNATIVE INVESTMENTS PRIVATE EQUITY & VC > Venture Capitalists | Investopedia
An equity market is a market in which shares of companies are issued and traded, either through exchanges or over-the-counter markets. Also known as the stock market, it is one of the most vital areas of a market economy. It gives companies access to capital to grow their business, and investors a piece of ownership in a company with the potential to realize gains in their investment based on the company's future performance.
The term stock market refers to several exchanges in which shares of publicly held companies are bought and sold.
Such financial activities are conducted through formal exchanges and via over-the-counter (OTC) marketplaces that operate under a defined set of regulations.
Both “stock market” and “stock exchange” are often used interchangeably.
Traders in the stock market buy or sell shares on one or more of the stock exchanges that are part of the overall stock market.
The leading U.S. stock exchanges include the New York Stock Exchange (NYSE) and the Nasdaq.
Stock markets are venues where buyers and sellers meet to exchange equity shares of public corporations.
Stock markets are components of a free-market economy because they enable democratized access to investor trading and exchange of capital.
Stock markets create efficient price discovery and efficient dealing.
The U.S. stock market is regulated by the Securities and Exchange Commission (SEC) and local regulatory bodies.
The stock market allows buyers and sellers of securities to meet, interact, and transact.
The markets allow for price discovery for shares of corporations and serve as a barometer for the overall economy.
Buyers and sellers are assured of a fair price, high degree of liquidity, and transparency as market participants compete in the open market.
“The stock market is not the economy” is a phrase that gets used quite often.
Many pundits argue that the fluctuations of the major indexes tasked with tracking the performance of the country’s biggest companies have little impact on the average American, as the majority of shares trade hands among the super-wealthy.
Moreover, the stock market can undergo bubbles emanating from hype, perception, and opinions of the agents that affect the stock prices.
However, these perceptions do not necessarily reflect the real trading of goods and services, which in aggregate constitutes the economy.
While it’s true that the richest Americans own most of the stocks, it’s wrong to assume that nobody else is affected by how they are valued. Record numbers of American households are investing their savings in equities to secure their financial futures.
(1)
The Federal Reserve Board’s triennial Survey of Consumer Finances (SCF) collects information about family income, net worth, balance sheet components, credit use, and other financial outcomes. (2)
The 2019 SCF reveals improvements in economic well-being among large parts of the income and wealth distributions since the previous time the survey was conducted in 2016, and many groups with historically lower income and wealth saw relatively large gains. (3)
(1)
(2) [ For a general description of the SCF data, see box 1, “The Data Used in This Article.” The appendix provides a summary of key technical aspects of the survey.]
The Federal Reserve Board’s triennial Survey of Consumer Finances (SCF) collects information about family incomes, net worth, balance sheet components, credit use, and other financial outcomes. [2]
The 2016 SCF reveals broad-based gains in income and net worth since the previous time the survey was conducted, in 2013. [3]
[1]
[2] See box 1, “The Data Used in This Article,” for a general description of the SCF data. The appendix to this article provides a summary of key technical aspects of the survey.
What Is the Securities and Exchange Commission (SEC)?
The U.S. Securities and Exchange Commission (SEC) is an independent federal government regulatory agency responsible for protecting investors, maintaining fair and orderly functioning of the securities markets, and facilitating capital formation. It was created by Congress in 1934 as the first federal regulator of the securities markets. The SEC promotes full public disclosure, protects investors against fraudulent and manipulative practices in the market, and monitors corporate takeover actions in the United States. It also approves registration statements for bookrunners among underwriting firms.
[1]
[1] LAWS & REGULATIONS > SEC > Securities and Exchange Commission (SEC) | Investopedia
Stock exchanges are marketplaces in which U.S.:
- financial securities,
- commodities,
- derivatives, and other
- financial instruments
are traded.
While in the past, traders and brokers used to meet physically in a stock exchange building to trade stocks, now most of the financial trading happens electronically and automatically.
Still, each exchange has its own unique listing requirements for companies that wish to join.
As a baseline, stock exchanges require regular:
- financial reports,
- audited earnings, and
- minimal capital requirements.
An exchange is a marketplace where securities, commodities, derivatives and other financial instruments are traded. The core function of an exchange is to ensure fair and orderly trading and the efficient dissemination of price information for any securities trading on that exchange. Exchanges give companies, governments, and other groups a platform from which to sell securities to the investing public.
A mutual fund is a financial vehicle that pools assets from shareholders to invest in securities like:
- stocks,
- bonds,
- money market instruments, and
- other assets.
Mutual funds are operated by professional money managers,
- who allocate:
- the fund's assets
- and attempt to produce:
- capital gains or
- income
for the fund's investors.
A mutual fund's portfolio is structured and maintained to match the investment objectives stated in its prospectus.
What Is a Rate of Return (RoR)?
A rate of return (RoR) is:
- the net gain or
- loss
of an investment
over a specified time period,
expressed as a percentage of the investment’s initial cost.
When calculating the rate of return,
- you are determining:
- the percentage change:
- from the beginning of the period until the end.
[1]
[1] CORPORATE FINANCE > FINANCIAL RATIOS > Rate of Return | Investopedia
The annual rate of return is the percentage change in the value of an investment.
For example: If you assume you earn a 10% annual rate of return, then you are assuming that the value of your investment will increase by 10% every year. So, if you invest $1,000 for 1 year, then your investment would be worth $1,100 at the end of the one year period, before subtracting expenses.
[1]
A prospectus is a formal document:
- required by and
- filed with
- the Securities and Exchange Commission (SEC)
that provides details about an investment offering to the public.
A prospectus is filed for offerings of:
- stocks,
- bonds, and
- mutual funds.
The prospectus can help investors make more informed investment decisions because it contains a host of relevant information about the investment or security.
In areas other than investing,:
- a prospectus is a printed document that advertises or describes an offering such as a:
- school,
- commercial enterprise,
- forthcoming book, etc.
All forms of prospectus exist to:
- attract or inform:
- clients,
- members,
- buyers, or
- investors.
A money manager is a person or financial firm:
- that manages the securities portfolio of:
- an individual or
- institutional investor.
Typically, a money manager employs people with various expertise ranging from:
- research and selection of:
- investment options to
- monitoring the assets and deciding when to sell them.
In return for a fee,:
- the money manager has the fiduciary duty:
- to choose and manage investments prudently for clients, including:
- developing an appropriate investment strategy and
- buying and selling securities
- to meet those goals.
A money manager may also be known as a:
- "portfolio manager,"
- "asset manager," or
- "investment manager."
A fiduciary is a person or organization that acts on behalf of another person or persons, putting their clients’ interests ahead of their own, with a duty to preserve good faith and trust.
Being a fiduciary thus requires being bound both legally and ethically to act in the other’s best interests.
The doctrine of utmost good faith, also known by its Latin name uberrimae fidei, is a minimum standard, legally obliging all parties entering a contract to act honestly and not mislead or withhold critical information from one another.
It applies to many everyday financial transactions and is one of the most fundamental doctrines in insurance law.
Insurance is a:
- contract,
- represented by a policy,
- in which a policyholder receives:
- financial protection or
- reimbursement against losses
- from an insurance company.
The company pools clients’ risks to make payments more affordable for the insured.
Most people have some insurance: for:
- their car,
- their house,
- their healthcare, or
- their life.
Insurance policies hedge against financial losses resulting from accidents, injury, or property damage. Insurance also helps cover costs associated with liability (legal responsibility) for damage or injury caused to a third party.
[1]
[1] PERSONAL FINANCE > INSURANCE | Investopedia
[2] Building Blocks Student Handout | What is insurance? | consumerfinance.gov
To hedge, in finance, is to:
- take an offsetting position in an:
- asset or investment
- that reduces the price risk:
- of an existing position.
A hedge is therefore a:
- trade:
- that is made with the purpose of reducing the risk of:
- adverse price movements in another asset.
Normally,:
- a hedge consists of :
- taking the opposite position:
- in a related security or
- in a derivative security
- based on the asset to be hedged.
TRADING STRATEGIES ADVANCED STRATEGIES & INSTRUMENTS | Investopedia
A portfolio is a collection of financial investments like:
- stocks,
- bonds,
- commodities,
- cash, and
- cash equivalents,
including:
- closed-end funds and
- exchange traded funds (ETFs).
People generally believe that:
- stocks,
- bonds, and
- cash
comprise the core of a portfolio.
Though this is often the case,:
- it does not need to be the rule.
A portfolio may contain a wide range of assets including:
- real estate,
- art, and
- private investments.
A closed-end fund is a type of mutual fund that issues a fixed number of shares through a single initial public offering (IPO) to raise capital for its initial investments.
Its shares can then be bought and sold on a stock exchange but no new shares will be created and no new money will flow into the fund.
In contrast, an open-ended fund, such as most mutual funds and exchange-traded funds (ETFs), accepts a constant flow of new investment capital. It issues new shares and buys back its own shares on demand.
Many municipal bond funds and some global investment funds are closed-end funds.
INVESTING > GUIDE TO MUTUAL FUNDS > Closed-End Fund | Investopedia
An exchange-traded fund (ETF) is a type of pooled investment security that operates much like a mutual fund. Typically, ETFs will track a particular index, sector, commodity, or other assets, but unlike mutual funds, ETFs can be purchased or sold on a stock exchange the same way that a regular stock can. An ETF can be structured to track anything from the price of an individual commodity to a large and diverse collection of securities. ETFs can even be structured to track specific investment strategies.
The first ETF was the SPDR S&P 500 ETF (SPY), which tracks the S&P 500 Index, and which remains an actively traded ETF today.
KEY TAKEAWAYS
- An exchange-traded fund (ETF) is a basket of securities that trades on an exchange just like a stock does.
- ETF share prices fluctuate all day as the ETF is bought and sold; this is different from mutual funds, which only trade once a day after the market closes.
- ETFs can contain all types of investments, including stocks, commodities, or bonds; some offer U.S.-only holdings, while others are international.
- ETFs offer low expense ratios and fewer broker commissions than buying the stocks individually.
An ETF is called an exchange-traded fund because it’s traded on an exchange just like stocks are.
The price of an ETF’s shares will change throughout the trading day as the shares are bought and sold on the market.
This is unlike mutual funds, which are not traded on an exchange, and which trade only once per day after the markets close.
Additionally, ETFs tend to be more cost-effective and more liquid compared to mutual funds.
[1]
Investors face a bewildering array of choices:
- stocks or bonds,
- domestic or international,
- different sectors and industries,
- value or growth, etc.
Deciding whether to buy a:
- mutual fund or
- exchange-traded fund (ETF)
may seem like a trivial consideration next to all the others,
but there are key differences between the two types of funds that can affect how much money you make and how you make it.
Both mutual funds and ETFs hold portfolios of stocks and/or bonds and occasionally something more exotic, such as:
- precious metals or
- commodities.
They must adhere to the same regulations concerning:
- what they can own,
- how much can be concentrated in one or a few holdings,
- how much money they can borrow in relation to the portfolio size, and more.
Beyond those elements, the paths diverge.
Some of the differences may seem obscure,
- but they can make one type of fund or the other a better fit for your needs.
KEY TAKEAWAYS
- Both mutual funds and ETFs hold portfolios of stocks and/or bonds and occasionally something more exotic, such as precious metals or commodities.
- Both can track indexes as well, however ETFs tend to be more cost effective and more liquid as they trade on exchanges like shares of stock.
- Mutual funds can provide some benefits such as active management and greater regulatory oversight, but only allow transactions once per day and tend to have higher costs.
[1]
[1] INVESTOPEDIA > INVESTING > Differences between ETFs and Mutual Funds | Investopedia
Liquidity refers to the:
- efficiency or
- ease
with which an:
- asset or
- security
can be converted into:
- ready cash
without affecting its market price.
The most liquid asset of all is cash itself.
TRADING SKILLS > TRADING BASIC EDUCATION > Liquidity | Investopedia
The market price is the current price at which an asset or service can be bought or sold.
The market price of an:
- asset or
- service
is determined by the:
- forces of supply and
- demand.
The price at which quantity supplied equals quantity demanded is the:
- market price.
The market price is used to calculate consumer and economic surplus.
Consumer surplus refers to the difference between the:
- highest price a consumer is willing to pay for a good and
- the actual price they do pay for the good, or
- the market price.
Economic surplus refers to two related quantities:
- consumer surplus and
- producer surplus.
Producer surplus may also be referred to as profit:
- it is the amount that producers benefit by selling at the market price
- (provided that the market price is higher than the least that they would be willing to sell for).
- Economic surplus is the sum total of consumer surplus and producer surplus.
In mainstream economics, consumer surplus is the difference between the highest price a consumer is willing to pay and the actual price they do pay for the good (which is the market price of the good). In other words, consumer surplus is the difference between what a consumer is willing to pay and what they actually pay for a good or service.
ECONOMICS > GUIDE TO MICROECONOMICS > calculate consumer and economic surplus | Investopedia
Consumer surplus is an economic measurement of consumer benefits resulting from market competition.
A consumer surplus happens when the price that consumers pay for a product or service is:
- less than the price they're willing to pay.
It's a measure of the additional benefit that consumers receive
- because they're paying less for something than what they were willing to pay.
A surplus describes the amount of an asset or resource that exceeds the portion that's actively utilized.
A surplus can refer to a host of different items, including:
- income,
- profits,
- capital, and
- goods.
In the context of inventories,:
- a surplus describes:
- products that remain sitting on store shelves, unpurchased.
In budgetary contexts,:
- a surplus occurs when:
- income earned exceeds expenses paid.
A budget surplus:
- can also occur within governments when there's:
- leftover tax revenue
- after all government programs
- are fully financed.
The term budget surplus refers to a situation that occurs when income exceeds expenditures.
The term is often used to describe a corporation or government's financial state, unlike individuals who have savings instead of budget surpluses.
A surplus indicates that a government's finances are being effectively managed.
The opposite of a budget surplus is a budget deficit, which commonly occurs when spending exceeds income.
KEY TAKEAWAYS
A budget surplus is when income or revenue exceeds expenditures.
Governments and companies with surpluses have additional money that can be reinvested or used to pay off debts.
The opposite of a surplus is a deficit, which occurs when spending exceeds revenues.
The last time the U.S. ran a budget surplus was in 2001 under President Bill Clinton.
The U.S. budget had a deficit of more than $421 billion as of January 2023.
[1]
[1] GOVERNMENT SPENDING & DEBT > GOVERNMENT SPENDING > Budget Surplus | Investopedia
plt.figure(figsize=(5,1))
sns.barplot(data=df, x='GICS Sector', y='Volatility', ci=False) ## Complete the code to choose the right variables
plt.xticks(rotation=90)
plt.show()
From the Above barplot of Volatility versus GICS Sector:
plt.figure(figsize=(15, 12))
numeric_columns = df.select_dtypes(include=np.number).columns.tolist()
for i, variable in enumerate(numeric_columns):
plt.subplot(3, 4, i + 1)
plt.boxplot(df[variable], whis=1.5)
plt.tight_layout()
plt.title(variable)
plt.show()
class
sklearn.preprocessing.StandardScaler(
*,
copy=True,
with_mean=True,
with_std=True
)
Standardize features by removing the mean and scaling to unit variance.
The standard score of a sample x is calculated as:
z = (x - u) / s
where u is the mean of the training samples or zero if with_mean=False, and s is the standard deviation of the training samples or one if with_std=False.
Centering and scaling happen independently on each feature by computing the relevant statistics on the samples in the training set. Mean and standard deviation are then stored to be used on later data using:
- transform.
sklearn.preprocessing.StandardScaler | transform(X, copy=None)
transform(X, copy=None)
Perform standardization by centering and scaling.
X:{array-like, sparse matrix of shape (n_samples, n_features)}
The data used to scale along the features axis.
copy : bool, default=None Copy the input X or not.
X_tr : {ndarray, sparse matrix} of shape (n_samples, n_features)
Transformed array.
Standardization of a dataset is a common requirement for many machine learning estimators: they might behave badly if the individual features do not more or less look like standard normally distributed data (e.g. Gaussian with 0 mean and unit variance).
For instance many elements used in the objective function of a learning algorithm (such as the RBF kernel of Support Vector Machines or the L1 and L2 regularizers of linear models) assume that all features are centered around 0 and have variance in the same order. If a feature has a variance that is orders of magnitude larger than others, it might dominate the objective function and make the estimator unable to learn from other features correctly as expected.
This scaler can also be applied to sparse CSR or CSC matrices by passing with_mean=False to avoid breaking the sparsity structure of the data.
Compressed Sparse formats CSR and CSC in Python%20and,with%20a%20column%2Dmajor%20order.)
Compressed sparse row (CSR) and
compressed sparse column (CSC)
are widely known and most used formats of sparse data structures.
Mainly, they are used for write-once-read-many tasks.
Compressed Sparse Column (CSC) format is almost identical,
Usually, the CSC is used when there are:
On the contrary,:
Internally, CSR is based on three NumPy arrays:
data is an array of corresponding nonzero valuesindices is array of column indicesindptr points to row starts in data and indices
~ length of indptr is:data and indices~ nonzero items of the i-th row are located data[indptr[i]:indptr[i+1]] with column indices indices[indptr[i]:indptr[i+1]]
If you’re new to the SciPy sparse matrix game, you might find yourself stymied by the ‘indptr’ array, which can be used to instantiate a csc_matrix or a csr_matrix object. Here I give an example to explain how to compute matrix through NumPy arrays.
6.3. Preprocessing data The sklearn.preprocessing package provides several common utility functions and transformer classes to change raw feature vectors into a representation that is more suitable for the downstream estimators.
In general, learning algorithms benefit from standardization of the data set. If some outliers are present in the set, robust scalers or transformers are more appropriate. The behaviors of the different scalers, transformers, and normalizers on a dataset containing marginal outliers is highlighted in Compare the effect of different scalers on data with outliers.
6.3.1. Standardization, or mean removal and variance scaling
Standardization of datasets is a common requirement for many machine learning estimators implemented in scikit-learn; they might behave badly if the individual features do not more or less look like standard normally distributed data: Gaussian with zero mean and unit variance.
In practice we often ignore the shape of the distribution and just transform the data to center it by removing the mean value of each feature, then scale it by dividing non-constant features by their standard deviation.
For instance, many elements used in the objective function of a learning algorithm (such as the RBF kernel of Support Vector Machines or the l1 and l2 regularizers of linear models) may assume that all features are centered around zero or have variance in the same order. If a feature has a variance that is orders of magnitude larger than others, it might dominate the objective function and make the estimator unable to learn from other features correctly as expected.
6.3.1. Standardization, or mean removal and variance scaling | Continued | Sklearn
>>> from sklearn import preprocessing
>>> import numpy as np
>>> X_train = np.array([[ 1., -1., 2.],
... [ 2., 0., 0.],
... [ 0., 1., -1.]])
>>> scaler = preprocessing.StandardScaler().fit(X_train)
>>> scaler
StandardScaler()
>>> scaler.mean_
array([1. ..., 0. ..., 0.33...])
>>> scaler.scale_
array([0.81..., 0.81..., 1.24...])
Per feature relative scaling of the data to achieve zero mean and unit variance. Generally this is calculated using np.sqrt(var_). If a variance is zero, we can’t achieve unit variance, and the data is left as-is, giving a scaling factor of 1.
scale_ is equal to None when with_std=False.
# scaling the data before clustering
scaler = StandardScaler()
subset = df[numeric_columns].copy() ## Complete the code to scale the data
subset_scaled = scaler.fit_transform(subset)
# creating a dataframe of the scaled data
subset_scaled_df = pd.DataFrame(subset_scaled, columns=subset.columns)
k_means_df = subset_scaled_df.copy()
clusters = range(1, 15)
meanDistortions = []
for k in clusters:
model = KMeans(n_clusters=k, random_state=1)
model.fit(subset_scaled_df)
prediction = model.predict(k_means_df)
distortion = (
sum(np.min(cdist(k_means_df, model.cluster_centers_, "euclidean"), axis=1))
/ k_means_df.shape[0]
)
meanDistortions.append(distortion)
print("Number of Clusters:", k, "\tAverage Distortion:", distortion)
plt.plot(clusters, meanDistortions, "bx-")
plt.xlabel("k")
plt.ylabel("Average Distortion")
plt.title("Selecting k with the Elbow Method", fontsize=20)
plt.show()
Number of Clusters: 1 Average Distortion: 2.5425069919221697 Number of Clusters: 2 Average Distortion: 2.382318498894466 Number of Clusters: 3 Average Distortion: 2.2692367155390745 Number of Clusters: 4 Average Distortion: 2.1745559827866363 Number of Clusters: 5 Average Distortion: 2.128799332840716 Number of Clusters: 6 Average Distortion: 2.080400099226289 Number of Clusters: 7 Average Distortion: 2.0289794220177395 Number of Clusters: 8 Average Distortion: 1.964144163389972 Number of Clusters: 9 Average Distortion: 1.9221492045198068 Number of Clusters: 10 Average Distortion: 1.8513913649973124 Number of Clusters: 11 Average Distortion: 1.8024134734578485 Number of Clusters: 12 Average Distortion: 1.7900931879652673 Number of Clusters: 13 Average Distortion: 1.7417609203336912 Number of Clusters: 14 Average Distortion: 1.673559857259703
model = KMeans(random_state=1)
visualizer = KElbowVisualizer(model, k=(1, 15), timings=True)
visualizer.fit(k_means_df) # fit the data to the visualizer
visualizer.show() # finalize and render figure
<Axes: title={'center': 'Distortion Score Elbow for KMeans Clustering'}, xlabel='k', ylabel='distortion score'>
sil_score = []
cluster_list = range(2, 15)
for n_clusters in cluster_list:
clusterer = KMeans(n_clusters=n_clusters, random_state=1)
preds = clusterer.fit_predict((subset_scaled_df))
score = silhouette_score(k_means_df, preds)
sil_score.append(score)
print("For n_clusters = {}, the silhouette score is {})".format(n_clusters, score))
plt.plot(cluster_list, sil_score)
plt.show()
For n_clusters = 2, the silhouette score is 0.43969639509980457) For n_clusters = 3, the silhouette score is 0.4644405674779404) For n_clusters = 4, the silhouette score is 0.4577225970476733) For n_clusters = 5, the silhouette score is 0.43228336443659804) For n_clusters = 6, the silhouette score is 0.4005422737213617) For n_clusters = 7, the silhouette score is 0.3976335364987305) For n_clusters = 8, the silhouette score is 0.40278401969450467) For n_clusters = 9, the silhouette score is 0.3778585981433699) For n_clusters = 10, the silhouette score is 0.13458938329968687) For n_clusters = 11, the silhouette score is 0.1421832155528444) For n_clusters = 12, the silhouette score is 0.2044669621527429) For n_clusters = 13, the silhouette score is 0.23424874810104204) For n_clusters = 14, the silhouette score is 0.12102526472829901)
model = KMeans(random_state=1)
visualizer = KElbowVisualizer(model, k=(2, 15), metric="silhouette", timings=True)
visualizer.fit(k_means_df) # fit the data to the visualizer
visualizer.show() # finalize and render figure
<Axes: title={'center': 'Silhouette Score Elbow for KMeans Clustering'}, xlabel='k', ylabel='silhouette score'>
# finding optimal no. of clusters with silhouette coefficients
visualizer = SilhouetteVisualizer(KMeans(6, random_state=1)) ## Complete the code to visualize the silhouette scores for certain number of clusters
visualizer.fit(k_means_df)
visualizer.show()
<Axes: title={'center': 'Silhouette Plot of KMeans Clustering for 340 Samples in 6 Centers'}, xlabel='silhouette coefficient values', ylabel='cluster label'>
# final K-means model
kmeans = KMeans(n_clusters=6, random_state=1) ## Complete the code to choose the number of clusters
kmeans.fit(k_means_df)
KMeans(n_clusters=6, random_state=1)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
KMeans(n_clusters=6, random_state=1)
# creating a copy of the original data
df1 = df.copy()
# adding kmeans cluster labels to the original and scaled dataframes
k_means_df["KM_segments"] = kmeans.labels_
df1["KM_segments"] = kmeans.labels_
df1.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 340 entries, 0 to 339 Data columns (total 16 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Ticker Symbol 340 non-null object 1 Security 340 non-null object 2 GICS Sector 340 non-null object 3 GICS Sub Industry 340 non-null object 4 Current Price 340 non-null float64 5 Price Change 340 non-null float64 6 Volatility 340 non-null float64 7 ROE 340 non-null int64 8 Cash Ratio 340 non-null int64 9 Net Cash Flow 340 non-null int64 10 Net Income 340 non-null int64 11 Earnings Per Share 340 non-null float64 12 Estimated Shares Outstanding 340 non-null float64 13 P/E Ratio 340 non-null float64 14 P/B Ratio 340 non-null float64 15 KM_segments 340 non-null int32 dtypes: float64(7), int32(1), int64(4), object(4) memory usage: 41.3+ KB
python boolean or
for debugging current issue: pandas.DataFrame.to_string
Error: TypeError: unsupported operand type(s) for |: 'str' and 'str'
Error: TypeError: unsupported operand type(s) for |: 'str' and 'str'
df1.columns
Index(['Ticker Symbol', 'Security', 'GICS Sector', 'GICS Sub Industry',
'Current Price', 'Price Change', 'Volatility', 'ROE', 'Cash Ratio',
'Net Cash Flow', 'Net Income', 'Earnings Per Share',
'Estimated Shares Outstanding', 'P/E Ratio', 'P/B Ratio',
'KM_segments'],
dtype='object')
df1_num_cols=[]
df1_obj_cols=[]
for iterable in df.columns:
if (df1.columns.dtype == 'float64' or 'int64' or 'int32'):
df1_num_cols.append(iterable)
# print(df1.columns.dtype == 'float64' or 'int64' or 'int32')
print(df1.columns.dtype)
print(df1.columns)
# print(iterable)
# print(df1.columns.dtype)
else:
df1_obj_cols.append(iterable)
object
Index(['Ticker Symbol', 'Security', 'GICS Sector', 'GICS Sub Industry',
'Current Price', 'Price Change', 'Volatility', 'ROE', 'Cash Ratio',
'Net Cash Flow', 'Net Income', 'Earnings Per Share',
'Estimated Shares Outstanding', 'P/E Ratio', 'P/B Ratio',
'KM_segments'],
dtype='object')
object
Index(['Ticker Symbol', 'Security', 'GICS Sector', 'GICS Sub Industry',
'Current Price', 'Price Change', 'Volatility', 'ROE', 'Cash Ratio',
'Net Cash Flow', 'Net Income', 'Earnings Per Share',
'Estimated Shares Outstanding', 'P/E Ratio', 'P/B Ratio',
'KM_segments'],
dtype='object')
object
Index(['Ticker Symbol', 'Security', 'GICS Sector', 'GICS Sub Industry',
'Current Price', 'Price Change', 'Volatility', 'ROE', 'Cash Ratio',
'Net Cash Flow', 'Net Income', 'Earnings Per Share',
'Estimated Shares Outstanding', 'P/E Ratio', 'P/B Ratio',
'KM_segments'],
dtype='object')
object
Index(['Ticker Symbol', 'Security', 'GICS Sector', 'GICS Sub Industry',
'Current Price', 'Price Change', 'Volatility', 'ROE', 'Cash Ratio',
'Net Cash Flow', 'Net Income', 'Earnings Per Share',
'Estimated Shares Outstanding', 'P/E Ratio', 'P/B Ratio',
'KM_segments'],
dtype='object')
object
Index(['Ticker Symbol', 'Security', 'GICS Sector', 'GICS Sub Industry',
'Current Price', 'Price Change', 'Volatility', 'ROE', 'Cash Ratio',
'Net Cash Flow', 'Net Income', 'Earnings Per Share',
'Estimated Shares Outstanding', 'P/E Ratio', 'P/B Ratio',
'KM_segments'],
dtype='object')
object
Index(['Ticker Symbol', 'Security', 'GICS Sector', 'GICS Sub Industry',
'Current Price', 'Price Change', 'Volatility', 'ROE', 'Cash Ratio',
'Net Cash Flow', 'Net Income', 'Earnings Per Share',
'Estimated Shares Outstanding', 'P/E Ratio', 'P/B Ratio',
'KM_segments'],
dtype='object')
object
Index(['Ticker Symbol', 'Security', 'GICS Sector', 'GICS Sub Industry',
'Current Price', 'Price Change', 'Volatility', 'ROE', 'Cash Ratio',
'Net Cash Flow', 'Net Income', 'Earnings Per Share',
'Estimated Shares Outstanding', 'P/E Ratio', 'P/B Ratio',
'KM_segments'],
dtype='object')
object
Index(['Ticker Symbol', 'Security', 'GICS Sector', 'GICS Sub Industry',
'Current Price', 'Price Change', 'Volatility', 'ROE', 'Cash Ratio',
'Net Cash Flow', 'Net Income', 'Earnings Per Share',
'Estimated Shares Outstanding', 'P/E Ratio', 'P/B Ratio',
'KM_segments'],
dtype='object')
object
Index(['Ticker Symbol', 'Security', 'GICS Sector', 'GICS Sub Industry',
'Current Price', 'Price Change', 'Volatility', 'ROE', 'Cash Ratio',
'Net Cash Flow', 'Net Income', 'Earnings Per Share',
'Estimated Shares Outstanding', 'P/E Ratio', 'P/B Ratio',
'KM_segments'],
dtype='object')
object
Index(['Ticker Symbol', 'Security', 'GICS Sector', 'GICS Sub Industry',
'Current Price', 'Price Change', 'Volatility', 'ROE', 'Cash Ratio',
'Net Cash Flow', 'Net Income', 'Earnings Per Share',
'Estimated Shares Outstanding', 'P/E Ratio', 'P/B Ratio',
'KM_segments'],
dtype='object')
object
Index(['Ticker Symbol', 'Security', 'GICS Sector', 'GICS Sub Industry',
'Current Price', 'Price Change', 'Volatility', 'ROE', 'Cash Ratio',
'Net Cash Flow', 'Net Income', 'Earnings Per Share',
'Estimated Shares Outstanding', 'P/E Ratio', 'P/B Ratio',
'KM_segments'],
dtype='object')
object
Index(['Ticker Symbol', 'Security', 'GICS Sector', 'GICS Sub Industry',
'Current Price', 'Price Change', 'Volatility', 'ROE', 'Cash Ratio',
'Net Cash Flow', 'Net Income', 'Earnings Per Share',
'Estimated Shares Outstanding', 'P/E Ratio', 'P/B Ratio',
'KM_segments'],
dtype='object')
object
Index(['Ticker Symbol', 'Security', 'GICS Sector', 'GICS Sub Industry',
'Current Price', 'Price Change', 'Volatility', 'ROE', 'Cash Ratio',
'Net Cash Flow', 'Net Income', 'Earnings Per Share',
'Estimated Shares Outstanding', 'P/E Ratio', 'P/B Ratio',
'KM_segments'],
dtype='object')
object
Index(['Ticker Symbol', 'Security', 'GICS Sector', 'GICS Sub Industry',
'Current Price', 'Price Change', 'Volatility', 'ROE', 'Cash Ratio',
'Net Cash Flow', 'Net Income', 'Earnings Per Share',
'Estimated Shares Outstanding', 'P/E Ratio', 'P/B Ratio',
'KM_segments'],
dtype='object')
object
Index(['Ticker Symbol', 'Security', 'GICS Sector', 'GICS Sub Industry',
'Current Price', 'Price Change', 'Volatility', 'ROE', 'Cash Ratio',
'Net Cash Flow', 'Net Income', 'Earnings Per Share',
'Estimated Shares Outstanding', 'P/E Ratio', 'P/B Ratio',
'KM_segments'],
dtype='object')
df1_num_cols
['Ticker Symbol', 'Security', 'GICS Sector', 'GICS Sub Industry', 'Current Price', 'Price Change', 'Volatility', 'ROE', 'Cash Ratio', 'Net Cash Flow', 'Net Income', 'Earnings Per Share', 'Estimated Shares Outstanding', 'P/E Ratio', 'P/B Ratio']
df1_obj_cols
[]
pandas.DataFrame.to_string scipy.sparse.csr_matrix
arrays = [['Falcon', 'Falcon', 'Parrot', 'Parrot'],
['Captive', 'Wild', 'Captive', 'Wild']]
index = pd.MultiIndex.from_arrays(arrays, names=('Animal', 'Type'))
df = pd.DataFrame({'Max Speed': [390., 350., 30., 20.]},
index=index)
df
Max Speed
Animal Type
Falcon Captive 390.0
Wild 350.0
Parrot Captive 30.0
Wild 20.0
df.groupby(level=0).mean()
Max Speed
Takeaway:
df1.head(1)
| Ticker Symbol | Security | GICS Sector | GICS Sub Industry | Current Price | Price Change | Volatility | ROE | Cash Ratio | Net Cash Flow | Net Income | Earnings Per Share | Estimated Shares Outstanding | P/E Ratio | P/B Ratio | KM_segments | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | AAL | American Airlines Group | Industrials | Airlines | 42.349998 | 9.999995 | 1.687151 | 135 | 51 | -604000000 | 7610000000 | 11.39 | 668129938.5 | 3.718174 | -8.784219 | 0 |
df1.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 340 entries, 0 to 339 Data columns (total 16 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Ticker Symbol 340 non-null object 1 Security 340 non-null object 2 GICS Sector 340 non-null object 3 GICS Sub Industry 340 non-null object 4 Current Price 340 non-null float64 5 Price Change 340 non-null float64 6 Volatility 340 non-null float64 7 ROE 340 non-null int64 8 Cash Ratio 340 non-null int64 9 Net Cash Flow 340 non-null int64 10 Net Income 340 non-null int64 11 Earnings Per Share 340 non-null float64 12 Estimated Shares Outstanding 340 non-null float64 13 P/E Ratio 340 non-null float64 14 P/B Ratio 340 non-null float64 15 KM_segments 340 non-null int32 dtypes: float64(7), int32(1), int64(4), object(4) memory usage: 41.3+ KB
From: 'notebooks/USL_W2_AdditionalCaseStudy_ProductSegmentation+%281%29+%281%29+%281%29.ipynb':
Pandas.mean() TypeError: Could not convert to numeric https://stackoverflow.com/questions/44522741/pandas-mean-typeerror-could-not-convert-to-numeric
Debugging [Resolved]: Search string: 'groupby keep strings but run mean could not convert to numeric'
data.groupby('month', as_index=False).agg({"duration": "sum"}) Use Pandas Groupby to Group and Summarise DataFrames
solution:
groupby all string columns
as follows:
but first:
Debugging[Resolved]:
search string: groupby ValueError: 2
Pandas groupby throwing up ValueError
Solution:
Remove:
, as_index=False
from
.groupby() passed into method call
df1.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 340 entries, 0 to 339 Data columns (total 16 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Ticker Symbol 340 non-null object 1 Security 340 non-null object 2 GICS Sector 340 non-null object 3 GICS Sub Industry 340 non-null object 4 Current Price 340 non-null float64 5 Price Change 340 non-null float64 6 Volatility 340 non-null float64 7 ROE 340 non-null int64 8 Cash Ratio 340 non-null int64 9 Net Cash Flow 340 non-null int64 10 Net Income 340 non-null int64 11 Earnings Per Share 340 non-null float64 12 Estimated Shares Outstanding 340 non-null float64 13 P/E Ratio 340 non-null float64 14 P/B Ratio 340 non-null float64 15 KM_segments 340 non-null int32 dtypes: float64(7), int32(1), int64(4), object(4) memory usage: 41.3+ KB
df1.groupby(["KM_segments", "Ticker Symbol", "Security", "GICS Sector", "GICS Sub Industry"])
<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000002CDB5755670>
df1.groupby(["KM_segments", "Ticker Symbol", "Security", "GICS Sector", "GICS Sub Industry"]).mean()
| Current Price | Price Change | Volatility | ROE | Cash Ratio | Net Cash Flow | Net Income | Earnings Per Share | Estimated Shares Outstanding | P/E Ratio | P/B Ratio | |||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| KM_segments | Ticker Symbol | Security | GICS Sector | GICS Sub Industry | |||||||||||
| 0 | AAL | American Airlines Group | Industrials | Airlines | 42.349998 | 9.999995 | 1.687151 | 135.0 | 51.0 | -604000000.0 | 7.610000e+09 | 11.39 | 6.681299e+08 | 3.718174 | -8.784219 |
| ABBV | AbbVie | Health Care | Pharmaceuticals | 59.240002 | 8.339433 | 2.197887 | 130.0 | 77.0 | 51000000.0 | 5.144000e+09 | 3.15 | 1.633016e+09 | 18.806350 | -8.750068 | |
| ABT | Abbott Laboratories | Health Care | Health Care Equipment | 44.910000 | 11.301121 | 1.273646 | 21.0 | 67.0 | 938000000.0 | 4.423000e+09 | 2.94 | 1.504422e+09 | 15.275510 | -0.394171 | |
| ADM | Archer-Daniels-Midland Co | Consumer Staples | Agricultural Products | 36.680000 | -12.017268 | 1.516493 | 10.0 | 49.0 | -189000000.0 | 1.849000e+09 | 2.99 | 6.183946e+08 | 12.267559 | 7.496831 | |
| AEE | Ameren Corp | Utilities | MultiUtilities | 43.230000 | 2.174424 | 1.124186 | 9.0 | 14.0 | 287000000.0 | 6.360000e+08 | 2.60 | 2.446154e+08 | 16.626923 | -0.719497 | |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 5 | SE | Spectra Energy Corp. | Energy | Oil & Gas Refining & Marketing & Transportation | 23.940001 | -9.898378 | 2.030786 | 3.0 | 6.0 | -2000000.0 | 1.960000e+08 | 0.29 | 6.758621e+08 | 82.551728 | -2.580408 |
| SWN | Southwestern Energy | Energy | Oil & Gas Exploration & Production | 7.110000 | -44.798137 | 4.580042 | 200.0 | 2.0 | -38000000.0 | -4.556000e+09 | -6.07 | 4.021417e+08 | 93.089287 | 1.273530 | |
| TDC | Teradata Corp. | Information Technology | Application Software | 26.420000 | -8.833678 | 2.730659 | 25.0 | 88.0 | 5000000.0 | -2.140000e+08 | -1.53 | 1.398693e+08 | 74.555557 | 4.068084 | |
| WMB | Williams Cos. | Energy | Oil & Gas Exploration & Production | 25.700001 | -30.988186 | 3.719560 | 9.0 | 4.0 | -140000000.0 | -5.710000e+08 | -0.76 | 7.513158e+08 | 93.089287 | -14.561121 | |
| XEC | Cimarex Energy | Energy | Oil & Gas Exploration & Production | 89.379997 | -14.403372 | 2.397940 | 86.0 | 190.0 | 373520000.0 | -2.408948e+09 | -25.92 | 9.293781e+07 | 93.089287 | 7.186128 |
340 rows × 11 columns
km_cluster_profile = df1.groupby(["KM_segments", "Ticker Symbol", "Security", "GICS Sector", "GICS Sub Industry"]).mean() ## Complete the code to groupby the cluster labels
km_cluster_profile["count_in_each_segment"] = (
df1.groupby(["KM_segments", "Ticker Symbol", "Security", "GICS Sector", "GICS Sub Industry"])["Security"].count().values ## Complete the code to groupby the cluster labels
)
km_cluster_profile.style.highlight_max(color="lightgreen", axis=0)
| Current Price | Price Change | Volatility | ROE | Cash Ratio | Net Cash Flow | Net Income | Earnings Per Share | Estimated Shares Outstanding | P/E Ratio | P/B Ratio | count_in_each_segment | |||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| KM_segments | Ticker Symbol | Security | GICS Sector | GICS Sub Industry | ||||||||||||
| 0 | AAL | American Airlines Group | Industrials | Airlines | 42.349998 | 9.999995 | 1.687151 | 135.000000 | 51.000000 | -604000000.000000 | 7610000000.000000 | 11.390000 | 668129938.500000 | 3.718174 | -8.784219 | 1 |
| ABBV | AbbVie | Health Care | Pharmaceuticals | 59.240002 | 8.339433 | 2.197887 | 130.000000 | 77.000000 | 51000000.000000 | 5144000000.000000 | 3.150000 | 1633015873.000000 | 18.806350 | -8.750068 | 1 | |
| ABT | Abbott Laboratories | Health Care | Health Care Equipment | 44.910000 | 11.301121 | 1.273646 | 21.000000 | 67.000000 | 938000000.000000 | 4423000000.000000 | 2.940000 | 1504421769.000000 | 15.275510 | -0.394171 | 1 | |
| ADM | Archer-Daniels-Midland Co | Consumer Staples | Agricultural Products | 36.680000 | -12.017268 | 1.516493 | 10.000000 | 49.000000 | -189000000.000000 | 1849000000.000000 | 2.990000 | 618394648.800000 | 12.267559 | 7.496831 | 1 | |
| AEE | Ameren Corp | Utilities | MultiUtilities | 43.230000 | 2.174424 | 1.124186 | 9.000000 | 14.000000 | 287000000.000000 | 636000000.000000 | 2.600000 | 244615384.600000 | 16.626923 | -0.719497 | 1 | |
| AEP | American Electric Power | Utilities | Electric Utilities | 58.270000 | 2.371753 | 1.068485 | 11.000000 | 9.000000 | 13900000.000000 | 2052300000.000000 | 3.130000 | 421897810.200000 | 18.456543 | -3.022649 | 1 | |
| AFL | AFLAC Inc | Financials | Life & Health Insurance | 59.900002 | 3.027181 | 1.048295 | 14.000000 | 99.000000 | -308000000.000000 | 2533000000.000000 | 5.880000 | 430782312.900000 | 10.187075 | -1.883912 | 1 | |
| AIG | American International Group, Inc. | Financials | Property & Casualty Insurance | 61.970001 | 8.358108 | 1.106965 | 2.000000 | 99.000000 | -129000000.000000 | 2196000000.000000 | 1.690000 | 1299408284.000000 | 36.668640 | -4.327138 | 1 | |
| AIV | Apartment Investment & Mgmt | Real Estate | REITs | 40.029999 | 7.578608 | 1.163334 | 15.000000 | 47.000000 | 21818000.000000 | 248710000.000000 | 1.520000 | 163625000.000000 | 26.335526 | -1.269332 | 1 | |
| AIZ | Assurant Inc | Financials | Multi-line Insurance | 80.540001 | 1.897773 | 1.112604 | 3.000000 | 99.000000 | -30351000.000000 | 141555000.000000 | 2.080000 | 68055288.460000 | 38.721154 | -4.072615 | 1 | |
| AJG | Arthur J. Gallagher & Co. | Financials | Insurance Brokers | 40.939999 | -0.606943 | 1.052050 | 10.000000 | 99.000000 | 166000000.000000 | 356800000.000000 | 2.070000 | 172367149.800000 | 19.777777 | -9.855706 | 1 | |
| AKAM | Akamai Technologies Inc | Information Technology | Internet Software & Services | 52.630001 | -23.790903 | 1.384502 | 10.000000 | 225.000000 | 50823000.000000 | 321406000.000000 | 1.800000 | 178558888.900000 | 29.238889 | 4.282358 | 1 | |
| ALB | Albemarle Corp | Materials | Specialty Chemicals | 56.009998 | 26.461948 | 1.974323 | 10.000000 | 13.000000 | -2276034000.000000 | 334906000.000000 | 3.010000 | 111264451.800000 | 18.607973 | -13.649723 | 1 | |
| ALK | Alaska Air Group Inc | Industrials | Airlines | 80.510002 | 2.066436 | 1.773431 | 35.000000 | 74.000000 | -34000000.000000 | 848000000.000000 | 6.610000 | 128290469.000000 | 12.180031 | -1.114658 | 1 | |
| ALL | Allstate Corp | Financials | Property & Casualty Insurance | 62.090000 | 6.592275 | 1.053266 | 11.000000 | 99.000000 | -162000000.000000 | 2171000000.000000 | 5.120000 | 424023437.500000 | 12.126953 | -4.327138 | 1 | |
| ALLE | Allegion | Industrials | Building Products | 65.919998 | 13.753230 | 1.283795 | 601.000000 | 45.000000 | -90800000.000000 | 153900000.000000 | 1.600000 | 96187500.000000 | 41.199999 | -0.877453 | 1 | |
| AMAT | Applied Materials Inc | Information Technology | Semiconductor Equipment | 18.670000 | 26.834239 | 1.460030 | 18.000000 | 131.000000 | 1795000000.000000 | 1377000000.000000 | 1.130000 | 1218584071.000000 | 16.522124 | 3.857756 | 1 | |
| AME | AMETEK Inc | Industrials | Electrical Components & Equipment | 53.590000 | 2.212474 | 1.089266 | 18.000000 | 37.000000 | 3390000.000000 | 590859000.000000 | 2.460000 | 240186585.400000 | 21.784553 | -4.490342 | 1 | |
| AMG | Affiliated Managers Group Inc | Financials | Asset Management & Custody Banks | 159.759995 | -6.611335 | 2.093065 | 18.000000 | 67.000000 | 13200000.000000 | 516000000.000000 | 9.490000 | 54373024.240000 | 16.834562 | -31.015380 | 1 | |
| AMP | Ameriprise Financial | Financials | Asset Management & Custody Banks | 106.419998 | -2.420686 | 1.222260 | 22.000000 | 67.000000 | -281000000.000000 | 1562000000.000000 | 8.600000 | 181627907.000000 | 12.374418 | -13.398380 | 1 | |
| AMT | American Tower Corp A | Real Estate | Specialized REITs | 96.949997 | 10.233087 | 1.165804 | 10.000000 | 39.000000 | 7194000.000000 | 685074000.000000 | 1.420000 | 482446478.900000 | 68.274646 | -20.813577 | 1 | |
| AN | AutoNation Inc | Consumer Discretionary | Specialty Stores | 59.660000 | 2.350316 | 1.480914 | 19.000000 | 1.000000 | -1300000.000000 | 442600000.000000 | 3.930000 | 112620865.100000 | 15.180662 | -7.970104 | 1 | |
| ANTM | Anthem Inc. | Health Care | Managed Health Care | 139.440002 | -0.620053 | 1.511654 | 11.000000 | 70.000000 | -38200000.000000 | 2560000000.000000 | 9.730000 | 263103802.700000 | 14.330935 | -31.006773 | 1 | |
| AON | Aon plc | Financials | Insurance Brokers | 92.209999 | 3.910301 | 1.105032 | 23.000000 | 99.000000 | 10000000.000000 | 1385000000.000000 | 4.930000 | 280933062.900000 | 18.703854 | -7.759856 | 1 | |
| APH | Amphenol Corp | Information Technology | Electronic Components | 52.230000 | 2.693667 | 1.007762 | 24.000000 | 175.000000 | 768300000.000000 | 763500000.000000 | 2.470000 | 309109311.700000 | 21.145749 | 8.202923 | 1 | |
| ATVI | Activision Blizzard | Information Technology | Home Entertainment Software | 38.709999 | 23.319529 | 1.886335 | 11.000000 | 70.000000 | -3025000000.000000 | 892000000.000000 | 1.210000 | 737190082.600000 | 31.991735 | 0.290291 | 1 | |
| AVB | AvalonBay Communities, Inc. | Real Estate | Residential REITs | 184.130005 | 4.857630 | 1.132875 | 8.000000 | 47.000000 | -108953000.000000 | 741733000.000000 | 5.540000 | 133886823.100000 | 33.236463 | -3.089477 | 1 | |
| AWK | American Water Works Company Inc | Utilities | Water Utilities | 59.750000 | 8.596874 | 1.171525 | 9.000000 | 4.000000 | 22000000.000000 | 476000000.000000 | 2.660000 | 178947368.400000 | 22.462406 | -4.895294 | 1 | |
| AXP | American Express Co | Financials | Consumer Finance | 69.550003 | -6.216290 | 0.900066 | 25.000000 | 99.000000 | 474000000.000000 | 5163000000.000000 | 3.900000 | 506660363.000000 | 10.263506 | -0.609074 | 1 | |
| BA | Boeing Company | Industrials | Aerospace & Defense | 144.589996 | 10.105078 | 1.155905 | 82.000000 | 24.000000 | -431000000.000000 | 5176000000.000000 | 7.520000 | 688297872.300000 | 19.227393 | 22.032612 | 1 | |
| BAX | Baxter International Inc. | Health Care | Health Care Equipment | 38.150002 | 16.702365 | 1.204526 | 11.000000 | 128.000000 | -712000000.000000 | 968000000.000000 | 1.780000 | 543820224.700000 | 21.432585 | 8.637045 | 1 | |
| BBT | BB&T Corporation | Financials | Banks | 37.810001 | 5.940045 | 1.077678 | 8.000000 | 99.000000 | 1386000000.000000 | 2084000000.000000 | 2.590000 | 804633204.600000 | 14.598456 | -0.852562 | 1 | |
| BCR | Bard (C.R.) Inc. | Health Care | Health Care Equipment | 189.440002 | 1.549182 | 1.394436 | 9.000000 | 82.000000 | -9600000.000000 | 135400000.000000 | 1.800000 | 75222222.220000 | 105.244446 | -4.127770 | 1 | |
| BIIB | BIOGEN IDEC Inc. | Health Care | Biotechnology | 306.350006 | 4.917982 | 1.825994 | 38.000000 | 133.000000 | 148900000.000000 | 3547000000.000000 | 15.380000 | 230624187.300000 | 19.918726 | 0.162602 | 1 | |
| BK | The Bank of New York Mellon Corp. | Financials | Banks | 41.220001 | 5.422003 | 1.201660 | 8.000000 | 99.000000 | -433000000.000000 | 3158000000.000000 | 2.730000 | 1156776557.000000 | 15.098901 | -3.321298 | 1 | |
| BLL | Ball Corp | Materials | Metal & Glass Containers | 72.730003 | 16.535816 | 1.386684 | 22.000000 | 10.000000 | 32600000.000000 | 280900000.000000 | 2.050000 | 137024390.200000 | 35.478050 | -3.895657 | 1 | |
| BMY | Bristol-Myers Squibb | Health Care | Health Care Distributors | 68.790001 | 16.081680 | 1.498872 | 11.000000 | 53.000000 | -3186000000.000000 | 1565000000.000000 | 0.940000 | 1664893617.000000 | 73.180852 | 0.588026 | 1 | |
| BSX | Boston Scientific | Health Care | Health Care Equipment | 18.440001 | 11.757582 | 1.491764 | 4.000000 | 13.000000 | -268000000.000000 | -239000000.000000 | -0.180000 | 1327777778.000000 | 31.468962 | -3.880921 | 1 | |
| BWA | BorgWarner | Consumer Discretionary | Auto Parts & Equipment | 43.230000 | 3.470563 | 2.058769 | 17.000000 | 25.000000 | -220100000.000000 | 609700000.000000 | 2.720000 | 224154411.800000 | 15.893382 | 1.044815 | 1 | |
| BXP | Boston Properties | Real Estate | REITs | 127.540001 | 7.203497 | 1.089469 | 10.000000 | 47.000000 | -1039361000.000000 | 583106000.000000 | 3.790000 | 153853825.900000 | 33.651715 | -1.269332 | 1 | |
| CAT | Caterpillar Inc. | Industrials | Construction & Farm Machinery & Heavy Trucks | 67.959999 | 3.550209 | 1.493553 | 17.000000 | 25.000000 | -881000000.000000 | 2512000000.000000 | 3.540000 | 709604519.800000 | 19.197740 | 6.264053 | 1 | |
| CB | Chubb Limited | Financials | Property & Casualty Insurance | 116.849998 | 13.193834 | 0.944847 | 10.000000 | 99.000000 | 1120000000.000000 | 2834000000.000000 | 8.710000 | 325373134.300000 | 13.415614 | -17.466101 | 1 | |
| CBG | CBRE Group | Real Estate | Real Estate Services | 34.580002 | 8.197757 | 1.297857 | 20.000000 | 12.000000 | -200481000.000000 | 547132000.000000 | 1.640000 | 333617073.200000 | 21.085367 | -3.415302 | 1 | |
| CCI | Crown Castle International Corp. | Real Estate | REITs | 86.449997 | 9.569068 | 0.960191 | 21.000000 | 36.000000 | 3190000.000000 | 1520992000.000000 | 4.440000 | 342565765.800000 | 19.470720 | -10.666679 | 1 | |
| CCL | Carnival Corp. | Consumer Discretionary | Hotels, Resorts & Cruise Lines | 54.480000 | 8.938217 | 1.347239 | 7.000000 | 20.000000 | 1064000000.000000 | 1757000000.000000 | 2.260000 | 777433628.300000 | 24.106195 | -7.477166 | 1 | |
| CF | CF Industries Holdings Inc | Materials | Fertilizers & Agricultural Chemicals | 40.810001 | -9.250611 | 2.368186 | 16.000000 | 25.000000 | -1710600000.000000 | 664900000.000000 | 2.970000 | 223872053.900000 | 13.740741 | -0.393528 | 1 | |
| CFG | Citizens Financial Group | Financials | Regional Banks | 26.190001 | 10.273688 | 1.189236 | 4.000000 | 99.000000 | -191000000.000000 | 840000000.000000 | 1.550000 | 541935483.900000 | 16.896775 | -0.063096 | 1 | |
| CHD | Church & Dwight | Consumer Staples | Household Products | 42.439999 | 1.047615 | 0.929026 | 20.000000 | 38.000000 | -93000000.000000 | 410400000.000000 | 3.130000 | 131118210.900000 | 13.559105 | -9.428134 | 1 | |
| CHRW | C. H. Robinson Worldwide | Industrials | Air Freight & Logistics | 62.020000 | -9.008221 | 1.185473 | 44.000000 | 12.000000 | 39289000.000000 | 509699000.000000 | 3.520000 | 144800852.300000 | 17.619318 | 1.117804 | 1 | |
| CHTR | Charter Communications | Consumer Discretionary | Cable & Satellite | 183.100006 | 3.598507 | 1.697942 | 589.000000 | 0.000000 | 2000000.000000 | -271000000.000000 | -2.430000 | 111522633.700000 | 20.819876 | -76.119077 | 1 | |
| CI | CIGNA Corp. | Health Care | Managed Health Care | 146.330002 | 8.682415 | 1.588398 | 17.000000 | 70.000000 | 548000000.000000 | 2094000000.000000 | 8.170000 | 256303549.600000 | 17.910649 | -8.805281 | 1 | |
| CINF | Cincinnati Financial | Financials | Property & Casualty Insurance | 59.169998 | 9.777358 | 0.935812 | 10.000000 | 99.000000 | -47000000.000000 | 634000000.000000 | 3.870000 | 163824289.400000 | 15.289405 | -4.327138 | 1 | |
| CL | Colgate-Palmolive | Consumer Staples | Household Products | 66.620003 | 4.781379 | 0.895471 | 463.000000 | 27.000000 | -119000000.000000 | 1384000000.000000 | 1.530000 | 904575163.400000 | 43.542486 | -0.548324 | 1 | |
| CMA | Comerica Inc. | Financials | Regional Banks | 41.830002 | 1.900129 | 1.557655 | 7.000000 | 99.000000 | 76000000.000000 | 521000000.000000 | 2.930000 | 177815699.700000 | 14.276451 | -0.063096 | 1 | |
| CME | CME Group Inc. | Financials | Financial Exchanges & Data | 90.599998 | -2.402245 | 1.323348 | 6.000000 | 99.000000 | 326500000.000000 | 1247000000.000000 | 3.710000 | 336118598.400000 | 24.420485 | -58.649536 | 1 | |
| CMI | Cummins Inc. | Industrials | Industrial Machinery | 88.010002 | -18.884791 | 1.472364 | 19.000000 | 48.000000 | -590000000.000000 | 1399000000.000000 | 7.860000 | 177989821.900000 | 11.197201 | 21.439428 | 1 | |
| CMS | CMS Energy | Utilities | MultiUtilities | 36.080002 | 1.949712 | 1.037844 | 14.000000 | 12.000000 | 39000000.000000 | 537000000.000000 | 1.900000 | 282631578.900000 | 18.989475 | 0.063687 | 1 | |
| CNC | Centene Corporation | Health Care | Managed Health Care | 65.809998 | 21.712591 | 2.298696 | 16.000000 | 70.000000 | 150000000.000000 | 355000000.000000 | 2.990000 | 118729097.000000 | 22.010033 | -1.305493 | 1 | |
| CNP | CenterPoint Energy | Utilities | MultiUtilities | 18.360001 | 1.436470 | 1.389867 | 20.000000 | 43.000000 | -34000000.000000 | -692000000.000000 | -1.610000 | 429813664.600000 | 17.313076 | 0.516503 | 1 | |
| COF | Capital One Financial | Financials | Consumer Finance | 72.180000 | -0.619575 | 1.364592 | 9.000000 | 99.000000 | 781000000.000000 | 4050000000.000000 | 7.150000 | 566433566.400000 | 10.095105 | -0.725643 | 1 | |
| COO | The Cooper Companies | Health Care | Health Care Supplies | 134.199997 | -9.672215 | 1.555057 | 8.000000 | 3.000000 | -8796000.000000 | 203523000.000000 | 1.790000 | 140335195.500000 | 33.994412 | 0.855096 | 1 | |
| CSX | CSX Corp. | Industrials | Railroads | 25.950001 | -4.349421 | 1.626219 | 17.000000 | 74.000000 | -41000000.000000 | 1968000000.000000 | 2.000000 | 984000000.000000 | 12.975001 | 0.902439 | 1 | |
| CTL | CenturyLink Inc | Telecommunications Services | Integrated Telecommunications Services | 25.160000 | 0.159232 | 1.522194 | 6.000000 | 3.000000 | -2000000.000000 | 878000000.000000 | 1.580000 | 555696202.500000 | 15.924051 | -13.383212 | 1 | |
| CTSH | Cognizant Technology Solutions | Information Technology | IT Consulting & Other Services | 60.020000 | -4.654489 | 1.338123 | 17.000000 | 182.000000 | 115100000.000000 | 1623600000.000000 | 2.670000 | 608089887.600000 | 22.479401 | 7.121644 | 1 | |
| CTXS | Citrix Systems | Information Technology | Internet Software & Services | 75.650002 | 9.021477 | 1.968864 | 16.000000 | 52.000000 | 108369000.000000 | 319361000.000000 | 2.010000 | 158886069.700000 | 37.636817 | -1.765013 | 1 | |
| CVS | CVS Health | Consumer Staples | Drug Retail | 97.769997 | 1.326561 | 1.487367 | 14.000000 | 11.000000 | -22000000.000000 | 5237000000.000000 | 4.660000 | 1123819742.000000 | 20.980686 | -7.019809 | 1 | |
| CVX | Chevron Corp. | Energy | Integrated Oil & Gas | 89.959999 | 12.844955 | 1.750655 | 3.000000 | 43.000000 | -1763000000.000000 | 4587000000.000000 | 2.460000 | 1864634146.000000 | 36.569105 | 4.763937 | 1 | |
| D | Dominion Resources | Utilities | Electric Utilities | 67.639999 | -3.988642 | 0.889931 | 15.000000 | 8.000000 | 289000000.000000 | 1899000000.000000 | 3.210000 | 591588785.000000 | 21.071651 | -7.604945 | 1 | |
| DAL | Delta Air Lines | Industrials | Airlines | 50.689999 | 13.375084 | 1.444219 | 42.000000 | 31.000000 | -116000000.000000 | 4526000000.000000 | 5.680000 | 796830985.900000 | 8.924296 | -16.730022 | 1 | |
| DD | Du Pont (E.I.) | Materials | Diversified Chemicals | 66.599998 | 37.489677 | 1.577881 | 20.000000 | 60.000000 | -1610000000.000000 | 1953000000.000000 | 2.170000 | 900000000.000000 | 30.691243 | 3.252222 | 1 | |
| DE | Deere & Co. | Industrials | Construction & Farm Machinery & Heavy Trucks | 76.269997 | 3.952561 | 1.551946 | 29.000000 | 22.000000 | 375200000.000000 | 1940000000.000000 | 4.030000 | 532235888.200000 | 14.842233 | 6.277287 | 1 | |
| DFS | Discover Financial Services | Financials | Consumer Finance | 53.619999 | 3.653584 | 1.159897 | 20.000000 | 99.000000 | 2288000000.000000 | 2297000000.000000 | 5.140000 | 446887159.500000 | 10.431906 | -0.375934 | 1 | |
| DGX | Quest Diagnostics | Health Care | Health Care Facilities | 71.139999 | 15.674795 | 1.381490 | 15.000000 | 11.000000 | -59000000.000000 | 709000000.000000 | 4.920000 | 144105691.100000 | 14.459349 | -4.552214 | 1 | |
| DHR | Danaher Corp. | Industrials | Industrial Conglomerates | 70.416985 | 8.924595 | 1.191466 | 14.000000 | 13.000000 | -2214800000.000000 | 3357400000.000000 | 4.810000 | 698004158.000000 | 14.639706 | -13.759230 | 1 | |
| DIS | The Walt Disney Company | Consumer Discretionary | Broadcasting & Cable TV | 105.080002 | 2.049141 | 1.188454 | 19.000000 | 26.000000 | 848000000.000000 | 8382000000.000000 | 4.950000 | 1693333333.000000 | 21.228283 | -3.985039 | 1 | |
| DISCA | Discovery Communications-A | Consumer Discretionary | Cable & Satellite | 26.680000 | 2.026769 | 1.689235 | 19.000000 | 25.000000 | 23000000.000000 | 1034000000.000000 | -2.430000 | 111522633.700000 | 20.819876 | -76.119077 | 1 | |
| DISCK | Discovery Communications-C | Consumer Discretionary | Cable & Satellite | 25.219999 | 3.572891 | 1.812144 | 19.000000 | 25.000000 | 23000000.000000 | 1034000000.000000 | -2.430000 | 111522633.700000 | 20.819876 | -76.119077 | 1 | |
| DLPH | Delphi Automotive | Consumer Discretionary | Auto Parts & Equipment | 85.730003 | 12.109326 | 1.440884 | 64.000000 | 14.000000 | -325000000.000000 | 1450000000.000000 | 5.080000 | 285433070.900000 | 16.875985 | -0.662152 | 1 | |
| DLR | Digital Realty Trust | Real Estate | Specialized REITs | 75.620003 | 15.573900 | 1.070406 | 7.000000 | 39.000000 | 22239000.000000 | 296689000.000000 | 1.560000 | 190185256.400000 | 48.474361 | -7.489313 | 1 | |
| DNB | Dun & Bradstreet | Industrials | Research & Consulting Services | 103.930000 | -1.188439 | 1.337924 | 15.000000 | 38.000000 | 46300000.000000 | 168800000.000000 | 4.680000 | 36068376.070000 | 22.207265 | -11.877441 | 1 | |
| DOV | Dover Corp. | Industrials | Industrial Machinery | 61.310001 | 6.979585 | 1.507569 | 24.000000 | 26.000000 | -319396000.000000 | 869829000.000000 | 5.520000 | 157577717.400000 | 11.106884 | -2.293440 | 1 | |
| DPS | Dr Pepper Snapple Group | Consumer Staples | Soft Drinks | 93.199997 | 18.049399 | 1.150797 | 35.000000 | 58.000000 | 683000000.000000 | 764000000.000000 | 4.000000 | 191000000.000000 | 23.299999 | -12.717277 | 1 | |
| DUK | Duke Energy | Utilities | Electric Utilities | 71.389999 | -0.833448 | 1.096727 | 7.000000 | 8.000000 | -1179000000.000000 | 2816000000.000000 | 4.050000 | 695308642.000000 | 17.627160 | -4.426811 | 1 | |
| DVA | DaVita Inc. | Health Care | Health Care Facilities | 69.709999 | -3.622291 | 1.211643 | 6.000000 | 79.000000 | 533875000.000000 | 269732000.000000 | 1.270000 | 212387401.600000 | 54.889763 | 1.962527 | 1 | |
| ECL | Ecolab Inc. | Materials | Specialty Chemicals | 114.379997 | 3.783684 | 1.078516 | 15.000000 | 2.000000 | -116800000.000000 | 1002100000.000000 | 3.380000 | 296479289.900000 | 33.840236 | -14.928867 | 1 | |
| ED | Consolidated Edison | Utilities | Electric Utilities | 64.269997 | -3.974306 | 1.068002 | 9.000000 | 20.000000 | 249000000.000000 | 1193000000.000000 | 4.070000 | 293120393.100000 | 15.791154 | -3.022649 | 1 | |
| EFX | Equifax Inc. | Industrials | Research & Consulting Services | 111.370003 | 14.531063 | 1.081040 | 19.000000 | 15.000000 | -35000000.000000 | 429100000.000000 | 3.610000 | 118864265.900000 | 30.850416 | -8.116821 | 1 | |
| EIX | Edison Int'l | Utilities | Electric Utilities | 59.209999 | -6.135071 | 0.927260 | 10.000000 | 3.000000 | 29000000.000000 | 1117000000.000000 | 3.130000 | 356869009.600000 | 18.916933 | -6.369284 | 1 | |
| EMN | Eastman Chemical | Materials | Diversified Chemicals | 67.510002 | 3.654238 | 1.404508 | 22.000000 | 14.000000 | 79000000.000000 | 848000000.000000 | 5.710000 | 148511383.500000 | 11.823118 | -12.308821 | 1 | |
| EQR | Equity Residential | Real Estate | REITs | 81.589996 | 8.037605 | 1.056186 | 8.000000 | 47.000000 | 2196000.000000 | 870120000.000000 | 2.370000 | 367139240.500000 | 34.426159 | -1.269332 | 1 | |
| ES | Eversource Energy | Utilities | MultiUtilities | 51.070000 | 0.709921 | 1.232829 | 8.000000 | 1.000000 | -14756000.000000 | 878485000.000000 | 2.770000 | 317142599.300000 | 18.436823 | -1.169833 | 1 | |
| ESS | Essex Property Trust, Inc. | Real Estate | Residential REITs | 239.410004 | 6.765073 | 1.118425 | 4.000000 | 47.000000 | 4073000.000000 | 232120000.000000 | 3.500000 | 66320000.000000 | 68.402858 | -3.089477 | 1 | |
| ETFC | E*Trade | Financials | Investment Banking & Brokerage | 29.639999 | 12.656785 | 1.452048 | 5.000000 | 99.000000 | 450000000.000000 | 268000000.000000 | 0.920000 | 291304347.800000 | 32.217390 | -0.597313 | 1 | |
| ETN | Eaton Corporation | Industrials | Industrial Conglomerates | 52.040001 | 1.166411 | 1.521430 | 13.000000 | 10.000000 | -513000000.000000 | 1979000000.000000 | 4.250000 | 465647058.800000 | 12.244706 | -8.639591 | 1 | |
| ETR | Entergy Corp. | Utilities | Electric Utilities | 68.360001 | 4.910983 | 1.217401 | 2.000000 | 44.000000 | -71065000.000000 | -156734000.000000 | -0.990000 | 158317171.700000 | 18.456543 | 6.174024 | 1 | |
| EXC | Exelon Corp. | Utilities | MultiUtilities | 27.770000 | -6.403775 | 1.351595 | 9.000000 | 74.000000 | 4624000000.000000 | 2269000000.000000 | 2.685000 | 299887089.100000 | 17.313076 | -1.715880 | 1 | |
| EXPD | Expeditors Int'l | Industrials | Air Freight & Logistics | 45.099998 | -4.449159 | 1.062553 | 27.000000 | 94.000000 | -119311000.000000 | 457223000.000000 | 2.420000 | 188935124.000000 | 18.636363 | 5.991459 | 1 | |
| EXPE | Expedia Inc. | Consumer Discretionary | Internet & Direct Marketing Retail | 124.300003 | 4.894517 | 1.578747 | 16.000000 | 29.000000 | 273599000.000000 | 764465000.000000 | 5.870000 | 130232538.300000 | 21.175469 | -44.103494 | 1 | |
| EXR | Extra Space Storage | Real Estate | Specialized REITs | 88.209999 | 13.922251 | 1.186059 | 19.000000 | 39.000000 | 28136000.000000 | 394950000.000000 | 1.580000 | 249968354.400000 | 55.829113 | -14.151445 | 1 | |
| FAST | Fastenal Co | Industrials | Building Products | 40.820000 | 10.984234 | 1.411396 | 29.000000 | 35.000000 | 14523000.000000 | 516361000.000000 | 1.770000 | 291729378.500000 | 23.062147 | 4.427425 | 1 | |
| FBHS | Fortune Brands Home & Security | Industrials | Building Products | 55.500000 | 16.817517 | 1.348297 | 13.000000 | 31.000000 | 46600000.000000 | 315000000.000000 | 1.970000 | 159898477.200000 | 28.172589 | -2.100708 | 1 | |
| FE | FirstEnergy Corp | Utilities | Electric Utilities | 31.730000 | 1.179844 | 1.238785 | 5.000000 | 2.000000 | 46000000.000000 | 578000000.000000 | 1.370000 | 421897810.200000 | 23.160584 | -6.072561 | 1 | |
| FIS | Fidelity National Information Services | Information Technology | Internet Software & Services | 60.599998 | -10.553508 | 1.148295 | 7.000000 | 29.000000 | 194800000.000000 | 650800000.000000 | 2.220000 | 293153153.200000 | 27.297296 | -19.086610 | 1 | |
| FISV | Fiserv Inc | Information Technology | Internet Software & Services | 91.459999 | 5.235295 | 0.904487 | 27.000000 | 18.000000 | -19000000.000000 | 712000000.000000 | 3.040000 | 234210526.300000 | 30.085526 | -7.975730 | 1 | |
| FLIR | FLIR Systems | Information Technology | Electronic Equipment & Instruments | 28.070000 | 0.214209 | 1.761193 | 15.000000 | 81.000000 | -58589000.000000 | 241686000.000000 | 1.730000 | 139702890.200000 | 16.225434 | 4.014713 | 1 | |
| FLR | Fluor Corp. | Industrials | Diversified Commercial Services | 47.220001 | 10.819056 | 1.774454 | 14.000000 | 73.000000 | -43239000.000000 | 412512000.000000 | 2.850000 | 144741052.600000 | 16.568421 | 14.992623 | 1 | |
| FLS | Flowserve Corporation | Industrials | Industrial Machinery | 42.080002 | 2.210357 | 1.781661 | 16.000000 | 27.000000 | -83906000.000000 | 267669000.000000 | 2.010000 | 133168656.700000 | 20.935324 | 6.746760 | 1 | |
| FMC | FMC Corporation | Materials | Diversified Chemicals | 39.130001 | 15.088238 | 2.175738 | 26.000000 | 5.000000 | -30900000.000000 | 489000000.000000 | 3.660000 | 133606557.400000 | 10.691257 | 5.101546 | 1 | |
| FRT | Federal Realty Investment Trust | Real Estate | Retail REITs | 146.100006 | 6.806063 | 1.239858 | 13.000000 | 47.000000 | -26905000.000000 | 210219000.000000 | 3.040000 | 69150986.840000 | 48.059213 | -3.973395 | 1 | |
| GD | General Dynamics | Industrials | Aerospace & Defense | 137.360001 | -0.463767 | 0.939544 | 28.000000 | 22.000000 | -1603000000.000000 | 2965000000.000000 | 9.230000 | 321235102.900000 | 14.881907 | 4.242998 | 1 | |
| GGP | General Growth Properties Inc. | Real Estate | Retail REITs | 27.209999 | 4.212937 | 1.390342 | 17.000000 | 47.000000 | -15576000.000000 | 1374561000.000000 | 3.040000 | 158299350.600000 | 47.366973 | -3.973395 | 1 | |
| GLW | Corning Inc. | Information Technology | Electronic Components | 18.280001 | 6.588927 | 1.578483 | 7.000000 | 163.000000 | -809000000.000000 | 1339000000.000000 | 1.020000 | 1312745098.000000 | 17.921570 | 3.617610 | 1 | |
| GM | General Motors | Consumer Discretionary | Automobile Manufacturers | 34.009998 | 12.281271 | 1.344514 | 24.000000 | 33.000000 | -3857000000.000000 | 9687000000.000000 | 6.110000 | 1585433715.000000 | 5.566284 | -4.892037 | 1 | |
| GPC | Genuine Parts | Consumer Discretionary | Specialty Stores | 85.889999 | 4.033432 | 1.177027 | 22.000000 | 5.000000 | 73901000.000000 | 705672000.000000 | 4.650000 | 151757419.400000 | 18.470968 | 7.205242 | 1 | |
| GRMN | Garmin Ltd. | Consumer Discretionary | Consumer Electronics | 37.169998 | 3.393594 | 1.665475 | 14.000000 | 121.000000 | -363198000.000000 | 456227000.000000 | 2.390000 | 190889958.200000 | 15.552300 | 5.760057 | 1 | |
| GT | Goodyear Tire & Rubber | Consumer Discretionary | Tires & Rubber | 32.669998 | 10.446241 | 1.522778 | 8.000000 | 30.000000 | -685000000.000000 | 307000000.000000 | 1.140000 | 269298245.600000 | 28.657893 | 3.835896 | 1 | |
| GWW | Grainger (W.W.) Inc. | Industrials | Industrial Materials | 202.589996 | -5.336199 | 1.348597 | 34.000000 | 16.000000 | 63492000.000000 | 768996000.000000 | 11.690000 | 65782378.100000 | 17.330196 | 12.112879 | 1 | |
| HAS | Hasbro Inc. | Consumer Discretionary | Leisure Products | 67.360001 | -7.076834 | 1.583355 | 27.000000 | 92.000000 | 83583000.000000 | 451838000.000000 | 3.610000 | 125162880.900000 | 18.659280 | 12.145326 | 1 | |
| HBAN | Huntington Bancshares | Financials | Banks | 11.060000 | 4.143126 | 1.337793 | 11.000000 | 99.000000 | -373409000.000000 | 692957000.000000 | 0.820000 | 845069512.200000 | 13.487805 | -0.065057 | 1 | |
| HCA | HCA Holdings | Health Care | Health Care Facilities | 67.629997 | -12.532337 | 1.914907 | 28.000000 | 13.000000 | 175000000.000000 | 2129000000.000000 | 5.140000 | 414202334.600000 | 13.157587 | -7.279051 | 1 | |
| HCN | Welltower Inc. | Real Estate | REITs | 68.029999 | 0.044116 | 1.341731 | 6.000000 | 47.000000 | -112818000.000000 | 849073000.000000 | 2.350000 | 361307659.600000 | 28.948936 | -3.738047 | 1 | |
| HCP | HCP Inc. | Real Estate | REITs | 34.826959 | 2.218658 | 1.282286 | 6.000000 | 47.000000 | 162690000.000000 | -559235000.000000 | -1.210000 | 462177686.000000 | 33.651715 | -1.269332 | 1 | |
| HIG | Hartford Financial Svc.Gp. | Financials | Property & Casualty Insurance | 43.459999 | -5.005467 | 1.147332 | 10.000000 | 99.000000 | 49000000.000000 | 1682000000.000000 | 4.050000 | 415308642.000000 | 10.730864 | -4.327138 | 1 | |
| HOG | Harley-Davidson | Consumer Discretionary | Motorcycle Manufacturers | 45.389999 | -17.247036 | 1.560372 | 41.000000 | 31.000000 | -184471000.000000 | 752207000.000000 | 3.710000 | 202751212.900000 | 12.234501 | 6.069389 | 1 | |
| HON | Honeywell Int'l Inc. | Industrials | Industrial Conglomerates | 103.570000 | 9.320247 | 1.103449 | 26.000000 | 41.000000 | -1504000000.000000 | 4768000000.000000 | 6.110000 | 780360065.500000 | 16.950900 | -3.709826 | 1 | |
| HPQ | HP Inc. | Information Technology | Computer Hardware | 11.840000 | 2.161759 | 2.373359 | 16.000000 | 18.000000 | 2300000000.000000 | 4554000000.000000 | 1.800000 | 513987730.100000 | 25.309524 | 3.954975 | 1 | |
| HRL | Hormel Foods Corp. | Consumer Staples | Packaged Foods & Meats | 39.540001 | 24.496225 | 1.078455 | 17.000000 | 29.000000 | 13065000.000000 | 686088000.000000 | 3.280000 | 199237804.900000 | 24.070121 | -1.980483 | 1 | |
| HSIC | Henry Schein | Health Care | Health Care Distributors | 158.190002 | 18.317132 | 1.013922 | 17.000000 | 3.000000 | -17388000.000000 | 479058000.000000 | 5.780000 | 82882006.920000 | 27.368512 | 5.925677 | 1 | |
| HST | Host Hotels & Resorts | Real Estate | REITs | 15.340000 | -3.217666 | 1.594628 | 8.000000 | 47.000000 | -445000000.000000 | 558000000.000000 | 0.220000 | 2536363636.000000 | 69.727273 | -0.113548 | 1 | |
| HSY | The Hershey Company | Consumer Staples | Packaged Foods & Meats | 89.269997 | -3.261814 | 1.188383 | 51.000000 | 16.000000 | -28325000.000000 | 512951000.000000 | 3.280000 | 199237804.900000 | 24.070121 | -1.980483 | 1 | |
| HUM | Humana Inc. | Health Care | Managed Health Care | 178.509995 | -0.145443 | 1.615206 | 12.000000 | 70.000000 | 636000000.000000 | 1276000000.000000 | 8.540000 | 149414519.900000 | 20.902810 | -8.805281 | 1 | |
| IBM | International Business Machines | Information Technology | IT Consulting & Other Services | 137.619995 | -5.292136 | 1.082881 | 92.000000 | 24.000000 | -790000000.000000 | 13190000000.000000 | 13.480000 | 978486646.900000 | 10.209198 | 4.852391 | 1 | |
| IDXX | IDEXX Laboratories | Health Care | Health Care Equipment | 72.919998 | -1.565880 | 1.469586 | 228.000000 | 40.000000 | -193542000.000000 | 192078000.000000 | 2.070000 | 92791304.350000 | 35.227052 | -0.981083 | 1 | |
| IFF | Intl Flavors & Fragrances | Materials | Specialty Chemicals | 119.639999 | 14.961083 | 1.152855 | 26.000000 | 25.000000 | -296585000.000000 | 419247000.000000 | 5.190000 | 80779768.790000 | 23.052023 | 5.047700 | 1 | |
| IP | International Paper | Materials | Paper Packaging | 37.700001 | -0.026513 | 1.301630 | 24.000000 | 27.000000 | -831000000.000000 | 938000000.000000 | 2.250000 | 416888888.900000 | 16.755556 | 6.123934 | 1 | |
| IPG | Interpublic Group | Consumer Discretionary | Advertising | 23.280001 | 21.821035 | 1.139799 | 23.000000 | 20.000000 | -157700000.000000 | 454600000.000000 | 1.110000 | 409549549.600000 | 20.972974 | 0.265658 | 1 | |
| IRM | Iron Mountain Incorporated | Real Estate | REITs | 27.010000 | -13.067267 | 1.301382 | 24.000000 | 15.000000 | 2448000.000000 | 123241000.000000 | 0.580000 | 212484482.800000 | 46.568966 | -2.763651 | 1 | |
| ITW | Illinois Tool Works | Industrials | Industrial Machinery | 92.680000 | 12.776831 | 1.142869 | 36.000000 | 130.000000 | -900000000.000000 | 1899000000.000000 | 5.160000 | 368023255.800000 | 17.961240 | 7.586477 | 1 | |
| IVZ | Invesco Ltd. | Financials | Asset Management & Custody Banks | 33.480000 | 7.067477 | 1.580839 | 12.000000 | 67.000000 | 412000000.000000 | 968100000.000000 | 2.260000 | 428362831.900000 | 14.814159 | 4.218620 | 1 | |
| JBHT | J. B. Hunt Transport Services | Industrials | Trucking | 73.360001 | 2.961405 | 1.218373 | 33.000000 | 1.000000 | -395000.000000 | 427235000.000000 | 3.690000 | 115781842.800000 | 19.880759 | 2.823845 | 1 | |
| JEC | Jacobs Engineering Group | Industrials | Industrial Conglomerates | 41.950001 | 11.539484 | 1.732990 | 7.000000 | 23.000000 | -271788000.000000 | 302971000.000000 | 2.420000 | 125194628.100000 | 17.334711 | 6.294943 | 1 | |
| JNPR | Juniper Networks | Information Technology | Networking Equipment | 27.600000 | 7.351229 | 1.841767 | 14.000000 | 108.000000 | -218700000.000000 | 633700000.000000 | 1.620000 | 391172839.500000 | 17.037037 | 2.752236 | 1 | |
| KIM | Kimco Realty | Real Estate | REITs | 26.459999 | 8.709938 | 1.224688 | 18.000000 | 47.000000 | 2212000.000000 | 894115000.000000 | 2.010000 | 444833333.300000 | 13.164179 | -1.269332 | 1 | |
| KMB | Kimberly-Clark | Consumer Staples | Household Products | 127.300003 | 17.511309 | 0.870405 | 582.000000 | 10.000000 | -170000000.000000 | 1013000000.000000 | 2.780000 | 364388489.200000 | 45.791368 | -2.533011 | 1 | |
| KSU | Kansas City Southern | Industrials | Railroads | 74.669998 | -18.438017 | 2.071639 | 12.000000 | 18.000000 | -211400000.000000 | 483500000.000000 | 4.410000 | 109637188.200000 | 16.931972 | -2.012091 | 1 | |
| LEG | Leggett & Platt | Industrials | Industrial Conglomerates | 42.020000 | 1.965545 | 1.204037 | 30.000000 | 36.000000 | -79600000.000000 | 329200000.000000 | 2.310000 | 142510822.500000 | 18.190476 | 2.931005 | 1 | |
| LEN | Lennar Corp. | Consumer Discretionary | Homebuilding | 48.910000 | 1.705136 | 1.569167 | 14.000000 | 25.000000 | -123369000.000000 | 802894000.000000 | 3.870000 | 207466149.900000 | 12.638243 | -0.307832 | 1 | |
| LH | Laboratory Corp. of America Holding | Health Care | Health Care Facilities | 123.639999 | 14.174899 | 1.603130 | 9.000000 | 42.000000 | 136400000.000000 | 436900000.000000 | 5.030000 | 178246546.300000 | 15.900937 | -1.294844 | 1 | |
| LKQ | LKQ Corporation | Consumer Discretionary | Distributors | 29.629999 | 4.441304 | 1.427237 | 14.000000 | 12.000000 | -27208000.000000 | 423223000.000000 | 3.030000 | 213598256.500000 | 20.819876 | -0.857290 | 1 | |
| LLL | L-3 Communications Holdings | Industrials | Industrial Conglomerates | 119.510002 | 14.539013 | 1.513434 | 6.000000 | 7.000000 | -235000000.000000 | -240000000.000000 | -2.970000 | 80808080.810000 | 17.334711 | 14.280750 | 1 | |
| LLY | Lilly (Eli) & Co. | Health Care | Pharmaceuticals | 84.260002 | 0.789478 | 1.440622 | 17.000000 | 54.000000 | -205200000.000000 | 2408400000.000000 | 2.270000 | 1060969163.000000 | 37.118944 | -0.651103 | 1 | |
| LMT | Lockheed Martin Corp. | Industrials | Aerospace & Defense | 217.149994 | 5.254227 | 0.903098 | 116.000000 | 8.000000 | -356000000.000000 | 3605000000.000000 | 11.620000 | 310240963.900000 | 18.687607 | -10.852854 | 1 | |
| LNT | Alliant Energy Corp | Utilities | Electric Utilities | 31.225001 | 6.642759 | 1.115842 | 10.000000 | 0.000000 | -51100000.000000 | 388400000.000000 | 3.360000 | 115595238.100000 | 9.293155 | -4.606591 | 1 | |
| LUK | Leucadia National Corp. | Financials | Multi-Sector Holdings | 17.389999 | -14.292764 | 1.554235 | 2.000000 | 81.000000 | -638127000.000000 | 252111000.000000 | 0.740000 | 340690540.500000 | 23.499999 | 19.821416 | 1 | |
| LUV | Southwest Airlines | Industrials | Airlines | 43.060001 | 13.855106 | 1.536290 | 30.000000 | 41.000000 | 301000000.000000 | 2181000000.000000 | 3.300000 | 660909090.900000 | 13.048485 | -5.117194 | 1 | |
| LVLT | Level 3 Communications | Telecommunications Services | Alternative Carriers | 54.360001 | 24.707504 | 1.457013 | 34.000000 | 61.000000 | 274000000.000000 | 3433000000.000000 | 9.710000 | 353553038.100000 | 5.598352 | -2.245773 | 1 | |
| LYB | LyondellBasell | Materials | Diversified Chemicals | 86.900002 | 2.573183 | 1.609745 | 68.000000 | 46.000000 | -107000000.000000 | 4476000000.000000 | 9.620000 | 465280665.300000 | 9.033264 | 10.316354 | 1 | |
| MA | Mastercard Inc. | Information Technology | Internet Software & Services | 97.360001 | 7.496965 | 1.095876 | 63.000000 | 130.000000 | 610000000.000000 | 3808000000.000000 | 3.360000 | 1133333333.000000 | 28.976191 | 3.451765 | 1 | |
| MAA | Mid-America Apartments | Real Estate | Residential REITs | 90.809998 | 10.622490 | 1.177776 | 12.000000 | 47.000000 | 10906000.000000 | 350745000.000000 | 4.410000 | 79534013.610000 | 20.591836 | -3.089477 | 1 | |
| MAC | Macerich | Real Estate | Retail REITs | 80.690002 | 4.183351 | 1.169328 | 10.000000 | 47.000000 | 1603000.000000 | 487562000.000000 | 3.080000 | 158299350.600000 | 26.198053 | -3.973395 | 1 | |
| MAR | Marriott Int'l. | Consumer Discretionary | Hotels, Resorts & Cruise Lines | 67.040001 | -1.973970 | 1.642450 | 24.000000 | 3.000000 | -8000000.000000 | 859000000.000000 | 3.220000 | 266770186.300000 | 20.819876 | -12.370198 | 1 | |
| MAS | Masco Corp. | Industrials | Building Products | 28.299999 | 11.637077 | 1.428359 | 263.000000 | 61.000000 | 85000000.000000 | 355000000.000000 | 1.030000 | 344660194.200000 | 27.475727 | 2.219577 | 1 | |
| MAT | Mattel Inc. | Consumer Discretionary | Leisure Products | 27.170000 | 30.625006 | 1.921708 | 14.000000 | 54.000000 | -78836000.000000 | 369416000.000000 | 1.080000 | 342051851.900000 | 25.157407 | 4.535251 | 1 | |
| MCO | Moody's Corp | Financials | Diversified Financial Services | 100.339996 | 2.345976 | 1.268800 | 167.000000 | 183.000000 | 537900000.000000 | 941300000.000000 | 4.700000 | 200276595.700000 | 21.348935 | 8.615585 | 1 | |
| MDLZ | Mondelez International | Consumer Staples | Packaged Foods & Meats | 44.840000 | 6.079962 | 1.321548 | 26.000000 | 17.000000 | 239000000.000000 | 7267000000.000000 | 4.490000 | 1618485523.000000 | 9.986637 | -12.809506 | 1 | |
| MET | MetLife Inc. | Financials | Life & Health Insurance | 48.209999 | 1.366690 | 1.138650 | 8.000000 | 99.000000 | 1944000000.000000 | 5310000000.000000 | 4.610000 | 1151843818.000000 | 10.457700 | -1.883912 | 1 | |
| MHK | Mohawk Industries | Consumer Discretionary | Home Furnishings | 189.389999 | 3.514425 | 1.492478 | 13.000000 | 3.000000 | -16185000.000000 | 615302000.000000 | 2.590000 | 237568339.800000 | 73.123552 | -3.980316 | 1 | |
| MJN | Mead Johnson | Consumer Staples | Packaged Foods & Meats | 78.949997 | 12.081196 | 1.718403 | 103.000000 | 136.000000 | 403700000.000000 | 653500000.000000 | 3.280000 | 199237804.900000 | 24.070121 | 6.495755 | 1 | |
| MKC | McCormick & Co. | Consumer Staples | Packaged Foods & Meats | 85.559998 | 6.976738 | 1.032221 | 24.000000 | 9.000000 | 35300000.000000 | 401600000.000000 | 3.140000 | 127898089.200000 | 27.248407 | -1.980483 | 1 | |
| MLM | Martin Marietta Materials | Materials | Construction Materials | 136.580002 | -10.866015 | 2.164150 | 7.000000 | 46.000000 | 59758000.000000 | 288792000.000000 | 4.310000 | 67005104.410000 | 31.689096 | 3.050887 | 1 | |
| MMC | Marsh & McLennan | Financials | Insurance Brokers | 55.450001 | 6.022948 | 1.034162 | 25.000000 | 99.000000 | -584000000.000000 | 1599000000.000000 | 3.010000 | 531229235.900000 | 18.421927 | -1.950194 | 1 | |
| MMM | 3M Company | Industrials | Industrial Conglomerates | 150.639999 | 5.927847 | 0.982698 | 42.000000 | 27.000000 | -99000000.000000 | 4833000000.000000 | 7.720000 | 626036269.400000 | 19.512953 | 2.023844 | 1 | |
| MO | Altria Group Inc | Consumer Staples | Tobacco | 58.209999 | 6.885788 | 0.959008 | 182.000000 | 33.000000 | -952000000.000000 | 5241000000.000000 | 2.670000 | 1962921348.000000 | 21.801498 | -6.632971 | 1 | |
| MPC | Marathon Petroleum | Energy | Oil & Gas Refining & Marketing & Transportation | 51.840000 | 11.507846 | 1.989371 | 22.000000 | 18.000000 | -367000000.000000 | 2852000000.000000 | 5.290000 | 539130434.800000 | 9.799622 | 5.798226 | 1 | |
| MRK | Merck & Co. | Health Care | Pharmaceuticals | 52.820000 | 7.031413 | 1.278460 | 10.000000 | 70.000000 | 1083000000.000000 | 4442000000.000000 | 1.580000 | 2811392405.000000 | 33.430380 | -4.282931 | 1 | |
| MTB | M&T Bank Corp. | Financials | Banks | 121.180000 | -0.361785 | 1.380390 | 7.000000 | 99.000000 | -5317000.000000 | 1079667000.000000 | 7.220000 | 149538365.700000 | 16.783934 | -0.938007 | 1 | |
| MTD | Mettler Toledo | Health Care | Life Sciences Tools & Services | 339.130005 | 18.942905 | 1.115376 | 61.000000 | 18.000000 | 13624000.000000 | 352820000.000000 | 12.750000 | 27672156.860000 | 26.598432 | 4.403994 | 1 | |
| MYL | Mylan N.V. | Health Care | Pharmaceuticals | 54.070000 | 33.177346 | 2.299304 | 9.000000 | 30.000000 | 1010500000.000000 | 847600000.000000 | 1.580000 | 1633015873.000000 | 33.430380 | -4.213309 | 1 | |
| NAVI | Navient | Financials | Consumer Finance | 11.450000 | 1.868327 | 2.230827 | 25.000000 | 99.000000 | 151000000.000000 | 997000000.000000 | 2.660000 | 374812030.100000 | 4.304511 | -1.880943 | 1 | |
| NDAQ | NASDAQ OMX Group | Financials | Diversified Financial Services | 58.169998 | 8.810324 | 1.563258 | 8.000000 | 117.000000 | -126000000.000000 | 428000000.000000 | 2.560000 | 167187500.000000 | 22.722655 | -11.717383 | 1 | |
| NEE | NextEra Energy | Utilities | MultiUtilities | 103.889999 | 6.237855 | 1.023375 | 12.000000 | 6.000000 | -6000000.000000 | 2752000000.000000 | 6.110000 | 450409165.300000 | 17.003273 | -7.353314 | 1 | |
| NLSN | Nielsen Holdings | Industrials | Research & Consulting Services | 46.599998 | 4.931317 | 1.198493 | 13.000000 | 21.000000 | 84000000.000000 | 570000000.000000 | 1.550000 | 367741935.500000 | 30.064515 | -12.375526 | 1 | |
| NSC | Norfolk Southern Corp. | Industrials | Railroads | 84.589996 | 9.529966 | 2.168814 | 13.000000 | 49.000000 | 128000000.000000 | 1556000000.000000 | 5.130000 | 303313840.200000 | 16.489278 | 0.926433 | 1 | |
| NTRS | Northern Trust Corp. | Financials | Asset Management & Custody Banks | 72.089996 | 5.796884 | 1.281566 | 11.000000 | 67.000000 | 3394000000.000000 | 973800000.000000 | 4.030000 | 241637717.100000 | 17.888336 | -13.398380 | 1 | |
| NUE | Nucor Corp. | Materials | Steel | 40.299999 | 6.585554 | 1.460619 | 5.000000 | 147.000000 | 915325000.000000 | 357659000.000000 | 1.110000 | 322215315.300000 | 36.306305 | 11.168107 | 1 | |
| NWL | Newell Brands | Consumer Discretionary | Housewares & Specialties | 44.080002 | 9.980039 | 1.641300 | 19.000000 | 14.000000 | 75400000.000000 | 350000000.000000 | 1.300000 | 269230769.200000 | 33.907694 | -2.075543 | 1 | |
| O | Realty Income Corporation | Real Estate | Retail REITs | 51.630001 | 8.420836 | 1.104581 | 4.000000 | 47.000000 | 36442000.000000 | 283766000.000000 | 1.090000 | 260335779.800000 | 47.366973 | -3.973395 | 1 | |
| OMC | Omnicom Group | Consumer Discretionary | Advertising | 75.660004 | 14.810321 | 1.066369 | 45.000000 | 18.000000 | 217100000.000000 | 1093900000.000000 | 4.430000 | 246930022.600000 | 17.079008 | -10.464098 | 1 | |
| ORLY | O'Reilly Automotive | Consumer Discretionary | Specialty Stores | 253.419998 | 0.964143 | 1.089370 | 47.000000 | 4.000000 | -134259000.000000 | 931216000.000000 | 9.320000 | 99915879.830000 | 27.190987 | -0.364026 | 1 | |
| PBCT | People's United Financial | Financials | Thrifts & Mortgage Finance | 16.150000 | 3.128991 | 1.132813 | 5.000000 | 99.000000 | -298400000.000000 | 260100000.000000 | 0.860000 | 302441860.500000 | 18.779070 | -0.426859 | 1 | |
| PBI | Pitney-Bowes | Industrials | Technology, Hardware, Software and Supplies | 20.650000 | 3.821021 | 1.259611 | 228.000000 | 34.000000 | -403561000.000000 | 407943000.000000 | 2.040000 | 199972058.800000 | 10.122549 | -0.733207 | 1 | |
| PCAR | PACCAR Inc. | Industrials | Construction & Farm Machinery & Heavy Trucks | 47.400002 | -9.317004 | 1.439564 | 23.000000 | 110.000000 | 278800000.000000 | 1604000000.000000 | 4.520000 | 354867256.600000 | 10.486726 | 6.290521 | 1 | |
| PCG | PG&E Corp. | Utilities | MultiUtilities | 53.189999 | 0.510206 | 1.039803 | 5.000000 | 6.000000 | -28000000.000000 | 888000000.000000 | 1.810000 | 490607734.800000 | 29.386740 | -1.121059 | 1 | |
| PEG | Public Serv. Enterprise Inc. | Utilities | Electric Utilities | 38.689999 | -8.230553 | 1.180661 | 13.000000 | 11.000000 | -8000000.000000 | 1679000000.000000 | 3.320000 | 505722891.600000 | 11.653614 | -0.361858 | 1 | |
| PEP | PepsiCo Inc. | Consumer Staples | Soft Drinks | 99.919998 | 6.072188 | 0.805357 | 45.000000 | 68.000000 | 2962000000.000000 | 5452000000.000000 | 3.710000 | 1469541779.000000 | 26.932614 | -5.190734 | 1 | |
| PFG | Principal Financial Group | Financials | Diversified Financial Services | 44.980000 | -5.305263 | 1.528985 | 13.000000 | 117.000000 | 700900000.000000 | 1234000000.000000 | 4.110000 | 300243309.000000 | 10.944039 | -4.526995 | 1 | |
| PG | Procter & Gamble | Consumer Staples | Personal Products | 79.410004 | 10.660538 | 0.806056 | 17.000000 | 129.000000 | 160383000.000000 | 636056000.000000 | 3.280000 | 491391569.000000 | 24.070121 | -2.256747 | 1 | |
| PGR | Progressive Corp. | Financials | Property & Casualty Insurance | 31.799999 | 3.515625 | 1.086898 | 17.000000 | 99.000000 | 116000000.000000 | 1267600000.000000 | 2.160000 | 586851851.900000 | 14.722222 | -0.843313 | 1 | |
| PHM | Pulte Homes Inc. | Consumer Discretionary | Homebuilding | 17.820000 | -5.564393 | 1.694751 | 10.000000 | 25.000000 | -533785000.000000 | 494090000.000000 | 1.380000 | 358036231.900000 | 12.913043 | -0.307832 | 1 | |
| PM | Philip Morris International | Consumer Staples | Tobacco | 87.910004 | 10.328820 | 0.861453 | 52.000000 | 22.000000 | 1735000000.000000 | 6873000000.000000 | 4.420000 | 1554977376.000000 | 19.889141 | -1.418027 | 1 | |
| PNC | PNC Financial Services | Financials | Banks | 95.309998 | 6.993709 | 1.120534 | 9.000000 | 99.000000 | -295000000.000000 | 4106000000.000000 | 7.520000 | 546010638.300000 | 12.674202 | -0.694126 | 1 | |
| PNR | Pentair Ltd. | Industrials | Industrial Machinery | 49.529999 | -3.034462 | 1.875910 | 2.000000 | 8.000000 | 15900000.000000 | -76400000.000000 | -0.420000 | 181904761.900000 | 14.579221 | -6.574869 | 1 | |
| PNW | Pinnacle West Capital | Utilities | MultiUtilities | 64.480003 | 0.498752 | 1.143421 | 10.000000 | 3.000000 | 31884000.000000 | 437257000.000000 | 3.940000 | 110978934.000000 | 16.365483 | -6.089228 | 1 | |
| PPG | PPG Industries | Materials | Diversified Chemicals | 98.820000 | 19.160738 | 1.533003 | 28.000000 | 31.000000 | 625000000.000000 | 1406000000.000000 | 5.180000 | 271428571.400000 | 19.077220 | -0.593158 | 1 | |
| PPL | PPL Corp. | Utilities | Electric Utilities | 34.130001 | 3.424245 | 1.109059 | 7.000000 | 22.000000 | -563000000.000000 | 682000000.000000 | 1.010000 | 675247524.800000 | 33.792080 | -2.827111 | 1 | |
| PRU | Prudential Financial | Financials | Diversified Financial Services | 81.410004 | 6.585503 | 1.227467 | 13.000000 | 117.000000 | 2694000000.000000 | 5642000000.000000 | 12.370000 | 456103476.200000 | 6.581245 | -4.178927 | 1 | |
| PSX | Phillips 66 | Energy | Oil & Gas Refining & Marketing & Transportation | 81.800003 | 5.371643 | 1.379589 | 18.000000 | 41.000000 | -2133000000.000000 | 4227000000.000000 | 7.780000 | 543316195.400000 | 10.514139 | 7.029056 | 1 | |
| PX | Praxair Inc. | Materials | Industrial Gases | 102.400002 | 0.293834 | 1.131240 | 35.000000 | 8.000000 | 21000000.000000 | 1547000000.000000 | 5.390000 | 287012987.000000 | 18.998145 | 0.574887 | 1 | |
| PYPL | PayPal | Information Technology | Data Processing & Outsourced Services | 36.200001 | 17.456201 | 1.925754 | 9.000000 | 25.000000 | -808000000.000000 | 1228000000.000000 | 1.000000 | 1228000000.000000 | 36.200001 | 5.434039 | 1 | |
| R | Ryder System | Industrials | Industrial Conglomerates | 56.830002 | -23.244191 | 1.945966 | 15.000000 | 4.000000 | 10853000.000000 | 304768000.000000 | 5.750000 | 53003130.430000 | 9.883479 | -12.020894 | 1 | |
| RCL | Royal Caribbean Cruises Ltd | Consumer Discretionary | Hotels, Resorts & Cruise Lines | 101.209999 | 13.425973 | 1.556512 | 8.000000 | 9.000000 | -67676000.000000 | 665783000.000000 | 3.030000 | 219730363.000000 | 33.402640 | -15.727481 | 1 | |
| RHI | Robert Half International | Industrials | Human Resource & Employment Services | 47.139999 | -7.659158 | 1.142370 | 36.000000 | 34.000000 | -62542000.000000 | 357796000.000000 | 2.720000 | 131542647.100000 | 17.330882 | 4.089472 | 1 | |
| ROP | Roper Industries | Industrials | Industrial Conglomerates | 189.789993 | 20.432767 | 1.058807 | 13.000000 | 108.000000 | 168081000.000000 | 696067000.000000 | 6.920000 | 100587716.800000 | 27.426300 | -16.215469 | 1 | |
| RSG | Republic Services Inc | Industrials | Industrial Conglomerates | 43.990002 | 6.745943 | 0.839821 | 10.000000 | 2.000000 | -42800000.000000 | 749900000.000000 | 2.140000 | 350420560.700000 | 20.556076 | -2.428225 | 1 | |
| SCG | SCANA Corp | Utilities | MultiUtilities | 60.490002 | 7.232764 | 1.266240 | 14.000000 | 9.000000 | 39000000.000000 | 746000000.000000 | 5.220000 | 142911877.400000 | 11.588123 | -4.016461 | 1 | |
| SCHW | Charles Schwab Corporation | Financials | Investment Banking & Brokerage | 32.930000 | 15.462833 | 1.456940 | 11.000000 | 99.000000 | 615000000.000000 | 1447000000.000000 | 1.040000 | 1391346154.000000 | 31.663462 | -0.130090 | 1 | |
| SEE | Sealed Air | Materials | Paper Packaging | 44.599998 | -5.146750 | 1.580117 | 43.000000 | 19.000000 | 73000000.000000 | 225400000.000000 | 1.630000 | 138282208.600000 | 27.361962 | -2.716908 | 1 | |
| SHW | Sherwin-Williams | Materials | Specialty Chemicals | 259.600006 | 16.537983 | 1.426488 | 121.000000 | 10.000000 | 165012000.000000 | 1053849000.000000 | 11.380000 | 92605360.280000 | 22.811951 | 2.825366 | 1 | |
| SLG | SL Green Realty | Real Estate | Office REITs | 112.980003 | 4.004424 | 1.091967 | 4.000000 | 47.000000 | -26010000.000000 | 284084000.000000 | 1.020000 | 278513725.500000 | 110.764709 | -3.089477 | 1 | |
| SNI | Scripps Networks Interactive Inc. | Consumer Discretionary | Broadcasting & Cable TV | 55.209999 | 12.238260 | 1.773865 | 40.000000 | 23.000000 | -654720000.000000 | 606828000.000000 | 4.680000 | 129664102.600000 | 11.797008 | -7.961579 | 1 | |
| SO | Southern Co. | Utilities | Electric Utilities | 46.790001 | 4.372070 | 0.895059 | 11.000000 | 15.000000 | 694000000.000000 | 2421000000.000000 | 2.600000 | 931153846.200000 | 17.996154 | -2.795456 | 1 | |
| SPG | Simon Property Group Inc | Real Estate | REITs | 194.440002 | 5.284822 | 1.135546 | 48.000000 | 47.000000 | 88852000.000000 | 2139375000.000000 | 5.880000 | 363839285.700000 | 33.068028 | -1.269332 | 1 | |
| SPGI | S&P Global, Inc. | Financials | Diversified Financial Services | 98.580002 | 14.044424 | 1.080858 | 596.000000 | 51.000000 | -1016000000.000000 | 1156000000.000000 | 4.260000 | 271361502.300000 | 23.140846 | -4.178927 | 1 | |
| SRCL | Stericycle Inc | Industrials | Industrial Conglomerates | 120.599998 | -13.906342 | 1.203816 | 10.000000 | 9.000000 | 33398000.000000 | 267046000.000000 | 3.020000 | 88425827.810000 | 39.933774 | -18.864194 | 1 | |
| SRE | Sempra Energy | Utilities | MultiUtilities | 94.010002 | -2.791849 | 1.126448 | 11.000000 | 9.000000 | -167000000.000000 | 1350000000.000000 | 5.430000 | 248618784.500000 | 17.313076 | -8.547222 | 1 | |
| STI | SunTrust Banks | Financials | Banks | 42.840000 | 11.970732 | 1.437938 | 8.000000 | 99.000000 | -2630000000.000000 | 1933000000.000000 | 3.620000 | 533977900.600000 | 11.834254 | -2.481376 | 1 | |
| STT | State Street Corp. | Financials | Diversified Financial Services | 66.360001 | -0.866449 | 1.444644 | 9.000000 | 117.000000 | -648000000.000000 | 1980000000.000000 | 4.530000 | 437086092.700000 | 14.649007 | -4.044970 | 1 | |
| SYF | Synchrony Financial | Financials | Consumer Finance | 30.410000 | -2.874478 | 1.835028 | 18.000000 | 99.000000 | 497000000.000000 | 2214000000.000000 | 2.660000 | 832330827.100000 | 11.432331 | -0.842213 | 1 | |
| SYK | Stryker Corp. | Health Care | Health Care Equipment | 92.940002 | -1.650792 | 1.138163 | 17.000000 | 116.000000 | 1584000000.000000 | 1439000000.000000 | 3.820000 | 376701570.700000 | 24.329843 | 7.026782 | 1 | |
| TAP | Molson Coors Brewing Company | Consumer Staples | Brewers | 93.919998 | 13.129368 | 1.217803 | 5.000000 | 35.000000 | -150300000.000000 | 359500000.000000 | 1.940000 | 185309278.400000 | 48.412370 | -25.385129 | 1 | |
| TGNA | Tegna, Inc. | Consumer Discretionary | Publishing | 25.520000 | 13.624226 | 1.797269 | 21.000000 | 21.000000 | 10716000.000000 | 459522000.000000 | 2.040000 | 225255882.400000 | 12.509804 | -12.726553 | 1 | |
| TMK | Torchmark Corp. | Financials | Life & Health Insurance | 57.160000 | 1.168142 | 1.022968 | 13.000000 | 99.000000 | -4636000.000000 | 527100000.000000 | 4.210000 | 125201900.200000 | 13.577197 | -1.883912 | 1 | |
| TMO | Thermo Fisher Scientific | Health Care | Health Care Equipment | 141.850006 | 15.607180 | 1.247751 | 9.000000 | 11.000000 | -891400000.000000 | 1975400000.000000 | 4.960000 | 398266129.000000 | 28.598792 | -28.032512 | 1 | |
| TRV | The Travelers Companies Inc. | Financials | Property & Casualty Insurance | 112.860001 | 13.029548 | 0.959365 | 15.000000 | 99.000000 | 6000000.000000 | 3439000000.000000 | 10.990000 | 312920837.100000 | 10.269336 | -0.891599 | 1 | |
| TSCO | Tractor Supply Company | Consumer Discretionary | Specialty Retail | 85.500000 | 1.303315 | 1.431109 | 29.000000 | 10.000000 | 12679000.000000 | 410395000.000000 | 3.030000 | 135443894.400000 | 28.217822 | 6.010954 | 1 | |
| TSN | Tyson Foods | Consumer Staples | Packaged Foods & Meats | 53.330002 | 23.249369 | 1.586719 | 13.000000 | 19.000000 | 250000000.000000 | 1220000000.000000 | 3.280000 | 199237804.900000 | 24.070121 | -1.980483 | 1 | |
| TSO | Tesoro Petroleum Co. | Energy | Oil & Gas Refining & Marketing & Transportation | 105.370003 | 8.584091 | 1.854132 | 30.000000 | 37.000000 | -58000000.000000 | 1540000000.000000 | 12.500000 | 123200000.000000 | 8.429600 | 4.594156 | 1 | |
| TSS | Total System Services | Information Technology | Internet Software & Services | 49.799999 | 9.234479 | 1.579248 | 20.000000 | 115.000000 | 100145000.000000 | 369041000.000000 | 1.980000 | 186384343.400000 | 25.151515 | -2.347391 | 1 | |
| TXN | Texas Instruments | Information Technology | Semiconductors | 54.810001 | 9.971912 | 1.263479 | 30.000000 | 126.000000 | -199000000.000000 | 2986000000.000000 | 2.860000 | 1044055944.000000 | 19.164336 | 2.768051 | 1 | |
| UAA | Under Armour | Consumer Discretionary | Apparel, Accessories & Luxury Goods | 80.610001 | -16.948277 | 1.758824 | 14.000000 | 27.000000 | -463323000.000000 | 232573000.000000 | 3.030000 | 213598256.500000 | 20.819876 | -0.857290 | 1 | |
| UAL | United Continental Holdings | Industrials | Airlines | 57.299999 | 8.215294 | 1.747606 | 82.000000 | 42.000000 | 1004000000.000000 | 7340000000.000000 | 19.520000 | 376024590.200000 | 2.935451 | -23.195292 | 1 | |
| UDR | UDR Inc | Real Estate | Residential REITs | 37.570000 | 8.583821 | 1.157906 | 12.000000 | 47.000000 | -8482000.000000 | 340383000.000000 | 1.300000 | 261833076.900000 | 28.900000 | -3.089477 | 1 | |
| UHS | Universal Health Services, Inc. | Health Care | Health Care Facilities | 119.489998 | -5.136552 | 2.048697 | 16.000000 | 6.000000 | 29159000.000000 | 680528000.000000 | 6.890000 | 98770391.870000 | 17.342525 | 6.255903 | 1 | |
| UNH | United Health Group Inc. | Health Care | Managed Health Care | 117.639999 | 1.466273 | 1.482349 | 17.000000 | 70.000000 | 3428000000.000000 | 5813000000.000000 | 6.100000 | 952950819.700000 | 19.285246 | -8.805281 | 1 | |
| UNM | Unum Group | Financials | Diversified Financial Services | 33.290001 | 3.804181 | 1.102848 | 10.000000 | 117.000000 | 10400000.000000 | 867100000.000000 | 3.510000 | 247037037.000000 | 9.484331 | -4.178927 | 1 | |
| UNP | Union Pacific | Industrials | Railroads | 78.199997 | -12.371135 | 1.430297 | 23.000000 | 43.000000 | -195000000.000000 | 4772000000.000000 | 5.510000 | 866061706.000000 | 14.192377 | 1.066899 | 1 | |
| UPS | United Parcel Service | Industrials | Air Freight & Logistics | 96.230003 | -2.797977 | 0.826408 | 196.000000 | 44.000000 | 439000000.000000 | 4844000000.000000 | 5.380000 | 900371747.200000 | 17.886618 | 1.069558 | 1 | |
| UTX | United Technologies | Industrials | Industrial Conglomerates | 96.070000 | 8.065239 | 0.949396 | 28.000000 | 31.000000 | 1630000000.000000 | 7608000000.000000 | 8.720000 | 872477064.200000 | 11.017202 | -13.198055 | 1 | |
| VAR | Varian Medical Systems | Health Care | Health Care Equipment | 80.800003 | 9.248248 | 1.034843 | 24.000000 | 61.000000 | -3800000.000000 | 411500000.000000 | 4.130000 | 99636803.870000 | 19.564165 | 9.471400 | 1 | |
| VLO | Valero Energy | Energy | Oil & Gas Refining & Marketing & Transportation | 70.709999 | 17.341522 | 1.626934 | 19.000000 | 56.000000 | 425000000.000000 | 3990000000.000000 | 8.000000 | 498750000.000000 | 8.838750 | 15.262155 | 1 | |
| VMC | Vulcan Materials | Materials | Construction Materials | 94.970001 | 6.028805 | 1.845710 | 5.000000 | 81.000000 | 142787000.000000 | 221177000.000000 | 1.660000 | 133239156.600000 | 57.210844 | -0.266191 | 1 | |
| VNO | Vornado Realty Trust | Real Estate | REITs | 99.959999 | 10.027519 | 1.019724 | 11.000000 | 47.000000 | 637230000.000000 | 760434000.000000 | 3.610000 | 210646537.400000 | 27.689750 | -1.081912 | 1 | |
| VRSK | Verisk Analytics | Industrials | Research & Consulting Services | 76.879997 | -1.448539 | 1.454019 | 37.000000 | 10.000000 | 98989000.000000 | 507577000.000000 | 3.070000 | 165334527.700000 | 25.042344 | -13.617440 | 1 | |
| VRSN | Verisign Inc. | Information Technology | Internet Software & Services | 87.360001 | 23.459580 | 1.379480 | 35.000000 | 127.000000 | 37051000.000000 | 375236000.000000 | 3.290000 | 114053495.400000 | 26.553192 | 4.076543 | 1 | |
| VTR | Ventas Inc | Real Estate | REITs | 56.430000 | 0.213104 | 1.444924 | 4.000000 | 47.000000 | -1803000.000000 | 419222000.000000 | 1.260000 | 332715873.000000 | 44.785714 | -4.040751 | 1 | |
| WEC | Wec Energy Group Inc | Utilities | Electric Utilities | 51.310001 | -1.986623 | 1.103033 | 7.000000 | 2.000000 | -12100000.000000 | 640300000.000000 | 2.360000 | 271313559.300000 | 21.741526 | -1.850995 | 1 | |
| WHR | Whirlpool Corp. | Consumer Discretionary | Household Appliances | 146.869995 | -0.230971 | 2.397803 | 17.000000 | 10.000000 | -254000000.000000 | 783000000.000000 | 9.950000 | 78693467.340000 | 14.760804 | -45.086335 | 1 | |
| WM | Waste Management Inc. | Industrials | Environmental Services | 53.369999 | 7.061186 | 0.940366 | 14.000000 | 2.000000 | -1268000000.000000 | 753000000.000000 | 1.660000 | 453614457.800000 | 32.150602 | -1.415299 | 1 | |
| WU | Western Union Co | Information Technology | Internet Software & Services | 17.910000 | -2.610109 | 1.273051 | 60.000000 | 16.000000 | -467300000.000000 | 837800000.000000 | 1.630000 | 513987730.100000 | 10.987730 | -8.043772 | 1 | |
| WY | Weyerhaeuser Corp. | Real Estate | REITs | 29.980000 | 8.544529 | 1.338067 | 10.000000 | 116.000000 | -568000000.000000 | 506000000.000000 | 0.890000 | 568539325.800000 | 33.685393 | 2.284802 | 1 | |
| WYN | Wyndham Worldwide | Consumer Discretionary | Hotels, Resorts & Cruise Lines | 72.650002 | 1.000976 | 1.331918 | 64.000000 | 9.000000 | -12000000.000000 | 612000000.000000 | 5.180000 | 118146718.100000 | 14.025097 | -10.249967 | 1 | |
| XEL | Xcel Energy Inc | Utilities | MultiUtilities | 35.910000 | 1.383405 | 1.015052 | 9.000000 | 2.000000 | 5332000.000000 | 984485000.000000 | 1.940000 | 507466494.800000 | 18.510309 | -2.261927 | 1 | |
| XL | XL Capital | Financials | Property & Casualty Insurance | 39.180000 | 7.696534 | 0.991011 | 10.000000 | 99.000000 | 734422000.000000 | 1201560000.000000 | 4.220000 | 284729857.800000 | 9.284360 | -7.762677 | 1 | |
| XRAY | Dentsply Sirona | Health Care | Health Care Supplies | 60.849998 | 19.901474 | 1.007230 | 11.000000 | 60.000000 | 133000000.000000 | 251200000.000000 | 1.790000 | 140335195.500000 | 33.994412 | 0.855096 | 1 | |
| XRX | Xerox Corp. | Information Technology | IT Consulting & Other Services | 10.630000 | 9.474768 | 1.866680 | 5.000000 | 26.000000 | -43000000.000000 | 474000000.000000 | 0.420000 | 1128571429.000000 | 25.309524 | -0.295949 | 1 | |
| XYL | Xylem Inc. | Industrials | Industrial Conglomerates | 36.500000 | 11.009729 | 1.166311 | 16.000000 | 83.000000 | 17000000.000000 | 340000000.000000 | 1.880000 | 180851063.800000 | 19.414894 | 4.130471 | 1 | |
| YUM | Yum! Brands Inc | Consumer Discretionary | Restaurants | 52.516175 | -8.698917 | 1.478877 | 142.000000 | 27.000000 | 159000000.000000 | 1293000000.000000 | 2.970000 | 435353535.400000 | 17.682214 | -3.838260 | 1 | |
| ZBH | Zimmer Biomet Holdings | Health Care | Health Care Equipment | 102.589996 | 9.347683 | 1.404206 | 1.000000 | 100.000000 | 376000000.000000 | 147000000.000000 | 0.780000 | 188461538.500000 | 131.525636 | -23.884449 | 1 | |
| ZION | Zions Bancorp | Financials | Regional Banks | 27.299999 | -1.158588 | 1.468176 | 4.000000 | 99.000000 | -43623000.000000 | 309471000.000000 | 1.200000 | 257892500.000000 | 22.749999 | -0.063096 | 1 | |
| ZTS | Zoetis | Health Care | Pharmaceuticals | 47.919998 | 16.678836 | 1.610285 | 32.000000 | 65.000000 | 272000000.000000 | 339000000.000000 | 0.680000 | 498529411.800000 | 70.470585 | 1.723068 | 1 | |
| 1 | C | Citigroup Inc. | Financials | Banks | 51.750000 | 4.714695 | 1.261984 | 8.000000 | 99.000000 | -11208000000.000000 | 17242000000.000000 | 5.410000 | 3187060998.000000 | 9.565619 | -1.167533 | 1 |
| F | Ford Motor | Consumer Discretionary | Automobile Manufacturers | 14.090000 | 2.398256 | 1.151454 | 26.000000 | 43.000000 | 3515000000.000000 | 7373000000.000000 | 1.860000 | 3963978495.000000 | 7.575269 | 5.108756 | 1 | |
| GILD | Gilead Sciences | Health Care | Biotechnology | 101.190002 | 2.689264 | 1.494060 | 98.000000 | 148.000000 | 2824000000.000000 | 18108000000.000000 | 12.370000 | 1463864188.000000 | 8.180275 | 3.159446 | 1 | |
| INTC | Intel Corp. | Information Technology | Semiconductors | 34.450001 | 14.035095 | 1.226022 | 19.000000 | 162.000000 | 12747000000.000000 | 11420000000.000000 | 2.410000 | 4738589212.000000 | 14.294606 | 3.954975 | 1 | |
| JPM | JPMorgan Chase & Co. | Financials | Banks | 66.029999 | 8.033377 | 1.130337 | 10.000000 | 99.000000 | -7341000000.000000 | 24442000000.000000 | 6.050000 | 4040000000.000000 | 10.914049 | -1.886881 | 1 | |
| KO | Coca Cola Company | Consumer Staples | Soft Drinks | 42.959999 | 6.812526 | 0.889913 | 29.000000 | 74.000000 | -1649000000.000000 | 7351000000.000000 | 1.690000 | 4349704142.000000 | 25.420118 | -1.466307 | 1 | |
| PFE | Pfizer Inc. | Health Care | Pharmaceuticals | 32.279999 | 3.130991 | 1.238748 | 11.000000 | 79.000000 | 298000000.000000 | 6960000000.000000 | 1.130000 | 6159292035.000000 | 28.566371 | -4.213309 | 1 | |
| T | AT&T Inc | Telecommunications Services | Integrated Telecommunications Services | 34.410000 | 5.942118 | 0.859442 | 11.000000 | 11.000000 | -3482000000.000000 | 13345000000.000000 | 2.370000 | 5630801688.000000 | 14.518987 | -23.537323 | 1 | |
| VZ | Verizon Communications | Telecommunications Services | Integrated Telecommunications Services | 46.220001 | 6.277303 | 0.842592 | 109.000000 | 14.000000 | -6128000000.000000 | 17879000000.000000 | 4.380000 | 4081963470.000000 | 10.552512 | -26.380687 | 1 | |
| WFC | Wells Fargo | Financials | Banks | 54.360001 | 5.532912 | 0.969774 | 12.000000 | 99.000000 | -460000000.000000 | 22894000000.000000 | 4.180000 | 5477033493.000000 | 13.004785 | -0.938007 | 1 | |
| XOM | Exxon Mobil Corp. | Energy | Integrated Oil & Gas | 77.949997 | 3.656915 | 1.370062 | 9.000000 | 7.000000 | -911000000.000000 | 16150000000.000000 | 3.850000 | 4194805195.000000 | 20.246752 | -2.706443 | 1 | |
| 2 | ADBE | Adobe Systems Inc | Information Technology | Application Software | 93.940002 | 13.977195 | 1.357679 | 9.000000 | 180.000000 | -240840000.000000 | 629551000.000000 | 1.260000 | 499643650.800000 | 74.555557 | 4.199651 | 1 |
| ADI | Analog Devices, Inc. | Information Technology | Semiconductors | 55.320000 | -1.827858 | 1.701169 | 14.000000 | 272.000000 | 315120000.000000 | 696878000.000000 | 0.310000 | 2247993548.000000 | 178.451613 | 1.059810 | 1 | |
| ADS | Alliance Data Systems | Information Technology | Data Processing & Outsourced Services | 276.570007 | 6.189286 | 1.116976 | 30.000000 | 25.000000 | 90885000.000000 | 596541000.000000 | 8.910000 | 66951851.850000 | 31.040405 | 129.064585 | 1 | |
| AMGN | Amgen Inc | Health Care | Biotechnology | 162.330002 | 17.163478 | 1.630259 | 25.000000 | 362.000000 | 413000000.000000 | 6939000000.000000 | 9.150000 | 758360655.700000 | 17.740984 | 24.012322 | 1 | |
| AVGO | Broadcom | Information Technology | Semiconductors | 145.149994 | 17.902683 | 1.847180 | 29.000000 | 163.000000 | 218000000.000000 | 1364000000.000000 | 2.860000 | 1044055944.000000 | 18.249407 | 3.954975 | 1 | |
| BAC | Bank of America Corp | Financials | Banks | 16.830000 | 8.440722 | 1.418688 | 6.000000 | 99.000000 | 20764000000.000000 | 15888000000.000000 | 4.180000 | 845069512.200000 | 13.004785 | -0.938007 | 1 | |
| CELG | Celgene Corp. | Health Care | Biotechnology | 119.760002 | 8.448793 | 2.000828 | 27.000000 | 333.000000 | 758700000.000000 | 1602000000.000000 | 2.020000 | 793069306.900000 | 59.287130 | -4.320051 | 1 | |
| CMG | Chipotle Mexican Grill | Consumer Discretionary | Restaurants | 479.850006 | -33.131268 | 2.474002 | 22.000000 | 237.000000 | -171460000.000000 | 475602000.000000 | 15.300000 | 31085098.040000 | 31.362745 | 17.201329 | 1 | |
| EBAY | eBay Inc. | Information Technology | Internet Software & Services | 27.480000 | 12.163265 | 1.409302 | 26.000000 | 271.000000 | -4496000000.000000 | 1725000000.000000 | 1.430000 | 1206293706.000000 | 19.216783 | 4.601699 | 1 | |
| EQIX | Equinix | Real Estate | REITs | 302.399994 | 10.019650 | 1.308082 | 7.000000 | 164.000000 | 1617921000.000000 | 187774000.000000 | 3.250000 | 57776615.380000 | 93.046152 | 23.856728 | 1 | |
| EW | Edwards Lifesciences | Health Care | Health Care Equipment | 78.980003 | 11.616734 | 1.666482 | 20.000000 | 257.000000 | 64600000.000000 | 494900000.000000 | 2.300000 | 215173913.000000 | 34.339132 | 6.349747 | 1 | |
| FB | Information Technology | Internet Software & Services | 104.660004 | 16.224320 | 1.320606 | 8.000000 | 958.000000 | 592000000.000000 | 3669000000.000000 | 1.310000 | 2800763359.000000 | 79.893133 | 5.884467 | 1 | ||
| FSLR | First Solar Inc | Information Technology | Semiconductors | 65.989998 | 55.051683 | 2.075216 | 10.000000 | 190.000000 | -355228000.000000 | 546421000.000000 | 5.420000 | 100815682.700000 | 12.175276 | 22.563791 | 1 | |
| FTR | Frontier Communications | Telecommunications Services | Integrated Telecommunications Services | 4.670000 | -2.301255 | 2.026818 | 3.000000 | 496.000000 | 254000000.000000 | -196000000.000000 | -0.290000 | 675862069.000000 | 14.518987 | 10.497704 | 1 | |
| HAL | Halliburton Co. | Energy | Oil & Gas Equipment & Services | 34.040001 | -5.101751 | 1.966062 | 4.000000 | 189.000000 | 7786000000.000000 | -671000000.000000 | -0.790000 | 849367088.600000 | 93.089287 | 17.345857 | 1 | |
| MCD | McDonald's Corp. | Consumer Discretionary | Restaurants | 118.139999 | 19.939085 | 0.733163 | 64.000000 | 260.000000 | 5607600000.000000 | 4529300000.000000 | 4.820000 | 939688796.700000 | 24.510373 | 7.122145 | 1 | |
| MNST | Monster Beverage | Consumer Staples | Soft Drinks | 49.653332 | 10.800357 | 1.585944 | 11.000000 | 568.000000 | 1805094000.000000 | 546733000.000000 | 3.710000 | 1469541779.000000 | 25.420118 | -5.190734 | 1 | |
| NEM | Newmont Mining Corp. (Hldg. Co.) | Materials | Gold | 17.990000 | 10.844116 | 2.536050 | 2.000000 | 198.000000 | 379000000.000000 | 220000000.000000 | 0.430000 | 511627907.000000 | 41.837209 | 6.971864 | 1 | |
| SWKS | Skyworks Solutions | Information Technology | Semiconductors | 76.830002 | -8.513933 | 2.017394 | 25.000000 | 225.000000 | 237800000.000000 | 798300000.000000 | 4.210000 | 189619952.500000 | 18.249407 | 7.413777 | 1 | |
| TRIP | TripAdvisor | Consumer Discretionary | Internet & Direct Marketing Retail | 85.250000 | 34.803917 | 1.578344 | 14.000000 | 212.000000 | 159000000.000000 | 198000000.000000 | 1.380000 | 143478260.900000 | 61.775362 | 2.627576 | 1 | |
| VRTX | Vertex Pharmaceuticals Inc | Health Care | Biotechnology | 125.830002 | 21.928300 | 2.456535 | 59.000000 | 221.000000 | 89509000.000000 | -556334000.000000 | -2.310000 | 240837229.400000 | 39.602928 | 2.559671 | 1 | |
| WAT | Waters Corporation | Health Care | Health Care Distributors | 134.580002 | 13.925341 | 1.044615 | 23.000000 | 425.000000 | 65488000.000000 | 469053000.000000 | 5.700000 | 82290000.000000 | 23.610527 | 29.547150 | 1 | |
| WYNN | Wynn Resorts Ltd | Consumer Discretionary | Casinos & Gaming | 69.190002 | 29.496541 | 3.794783 | 174.000000 | 198.000000 | -102075000.000000 | 195290000.000000 | 1.930000 | 101186528.500000 | 35.849742 | 12.695712 | 1 | |
| YHOO | Yahoo Inc. | Information Technology | Internet Software & Services | 33.259998 | 14.887727 | 1.845149 | 15.000000 | 459.000000 | -1032187000.000000 | -4359082000.000000 | -4.640000 | 939457327.600000 | 28.976191 | 6.261775 | 1 | |
| 3 | ALXN | Alexion Pharmaceuticals | Health Care | Biotechnology | 190.750000 | 22.338380 | 2.022921 | 2.000000 | 195.000000 | 66000000.000000 | 144000000.000000 | 0.680000 | 211764705.900000 | 280.514706 | -14.171389 | 1 |
| AMZN | Amazon.com Inc | Consumer Discretionary | Internet & Direct Marketing Retail | 675.890015 | 32.268105 | 1.460386 | 4.000000 | 58.000000 | 1333000000.000000 | 596000000.000000 | 1.280000 | 465625000.000000 | 528.039074 | 3.904430 | 1 | |
| ISRG | Intuitive Surgical Inc. | Health Care | Health Care Equipment | 546.159973 | 18.733013 | 1.126009 | 14.000000 | 317.000000 | 114300000.000000 | 588800000.000000 | 15.870000 | 37101449.280000 | 34.414617 | 42.607500 | 1 | |
| NFLX | Netflix Inc. | Information Technology | Internet Software & Services | 114.379997 | 11.145654 | 2.605949 | 6.000000 | 65.000000 | 695722000.000000 | 122641000.000000 | 0.290000 | 422900000.000000 | 394.413783 | -5.700168 | 1 | |
| PCLN | Priceline.com Inc | Consumer Discretionary | Internet & Direct Marketing Retail | 1274.949951 | 3.190527 | 1.268340 | 29.000000 | 184.000000 | -1671386000.000000 | 2551360000.000000 | 50.090000 | 50935516.070000 | 25.453183 | -1.052429 | 1 | |
| REGN | Regeneron | Health Care | Biotechnology | 542.869995 | 16.995320 | 1.802345 | 17.000000 | 129.000000 | 160383000.000000 | 636056000.000000 | 6.170000 | 103088492.700000 | 87.985412 | 20.409000 | 1 | |
| 4 | APA | Apache Corporation | Energy | Oil & Gas Exploration & Production | 44.470001 | 11.397804 | 2.405408 | 917.000000 | 80.000000 | 698000000.000000 | -23528000000.000000 | -61.200000 | 384444444.400000 | 93.089287 | 4.970809 | 1 |
| CHK | Chesapeake Energy | Energy | Integrated Oil & Gas | 4.500000 | -38.101788 | 4.559815 | 687.000000 | 22.000000 | -3283000000.000000 | -14685000000.000000 | -22.430000 | 654703522.100000 | 28.407929 | -1.840528 | 1 | |
| 5 | APC | Anadarko Petroleum Corp | Energy | Oil & Gas Exploration & Production | 48.580002 | -20.802083 | 2.435165 | 52.000000 | 22.000000 | -6430000000.000000 | -6692000000.000000 | -13.180000 | 507738998.500000 | 93.089287 | -12.860938 | 1 |
| ARNC | Arconic Inc | Industrials | Aerospace & Defense | 7.398807 | 1.647784 | 2.592065 | 3.000000 | 37.000000 | 42000000.000000 | -322000000.000000 | -0.310000 | 1038709677.000000 | 18.687607 | 2.639814 | 1 | |
| BHI | Baker Hughes Inc | Energy | Oil & Gas Equipment & Services | 46.150002 | -12.312367 | 2.559553 | 12.000000 | 84.000000 | 584000000.000000 | -1967000000.000000 | -4.490000 | 438084632.500000 | 93.089287 | 13.490544 | 1 | |
| COG | Cabot Oil & Gas | Energy | Oil & Gas Exploration & Production | 17.690001 | -20.099360 | 3.055818 | 6.000000 | 0.000000 | -20440000.000000 | -113891000.000000 | -0.280000 | 406753571.400000 | 93.089287 | -0.223147 | 1 | |
| CXO | Concho Resources | Energy | Oil & Gas Exploration & Production | 92.860001 | -2.744032 | 2.692546 | 1.000000 | 38.000000 | 228529000.000000 | 65900000.000000 | 0.540000 | 122037037.000000 | 171.962965 | 5.673991 | 1 | |
| DVN | Devon Energy Corp. | Energy | Oil & Gas Exploration & Production | 32.000000 | -15.478079 | 2.923698 | 205.000000 | 70.000000 | 830000000.000000 | -14454000000.000000 | -35.550000 | 406582278.500000 | 93.089287 | 1.785616 | 1 | |
| EOG | EOG Resources | Energy | Oil & Gas Exploration & Production | 70.790001 | -4.078593 | 1.941104 | 35.000000 | 39.000000 | -1368707000.000000 | -4524515000.000000 | -8.290000 | 545779855.200000 | 93.089287 | 1.416243 | 1 | |
| EQT | EQT Corporation | Energy | Oil & Gas Exploration & Production | 52.130001 | -21.253771 | 2.364883 | 2.000000 | 201.000000 | 523803000.000000 | 85171000.000000 | 0.560000 | 152091071.400000 | 93.089287 | 9.567952 | 1 | |
| FCX | Freeport-McMoran Cp & Gld | Materials | Copper | 6.770000 | -31.685167 | 3.796410 | 155.000000 | 5.000000 | -240000000.000000 | -12156000000.000000 | -11.310000 | 1074801061.000000 | 22.811951 | 2.935427 | 1 | |
| HES | Hess Corporation | Energy | Integrated Oil & Gas | 48.480000 | -4.585713 | 2.398580 | 16.000000 | 103.000000 | 272000000.000000 | -3056000000.000000 | -10.780000 | 283487940.600000 | 28.407929 | 6.264817 | 1 | |
| HPE | Hewlett Packard Enterprise | Information Technology | Technology Hardware, Storage & Peripherals | 15.200000 | -17.837838 | 3.400491 | 7.000000 | 45.000000 | 7523000000.000000 | 2461000000.000000 | 1.800000 | 513987730.100000 | 25.309524 | 3.954975 | 1 | |
| KMI | Kinder Morgan | Energy | Oil & Gas Refining & Marketing & Transportation | 14.920000 | -47.129693 | 3.139352 | 1.000000 | 7.000000 | -86000000.000000 | 253000000.000000 | 0.100000 | 2530000000.000000 | 149.200000 | -1.894071 | 1 | |
| MOS | The Mosaic Company | Materials | Fertilizers & Agricultural Chemicals | 27.590000 | -11.229086 | 2.830675 | 10.000000 | 62.000000 | -1098300000.000000 | 1000400000.000000 | 2.790000 | 358566308.200000 | 9.888889 | 5.846617 | 1 | |
| MRO | Marathon Oil Corp. | Energy | Oil & Gas Exploration & Production | 12.590000 | -20.265991 | 3.325386 | 12.000000 | 71.000000 | -1177000000.000000 | -2204000000.000000 | -3.260000 | 676073619.600000 | 93.089287 | 1.273530 | 1 | |
| MUR | Murphy Oil | Energy | Integrated Oil & Gas | 22.450001 | -8.591197 | 2.851180 | 43.000000 | 27.000000 | -910125000.000000 | -2270833000.000000 | -13.030000 | 174277283.200000 | 28.407929 | -1.298006 | 1 | |
| NBL | Noble Energy Inc | Energy | Oil & Gas Exploration & Production | 32.930000 | 7.298791 | 2.509437 | 24.000000 | 57.000000 | -155000000.000000 | -2441000000.000000 | -6.070000 | 402141680.400000 | 93.089287 | 1.171229 | 1 | |
| NFX | Newfield Exploration Co | Energy | Oil & Gas Exploration & Production | 32.560001 | -3.296695 | 2.421529 | 244.000000 | 1.000000 | -9000000.000000 | -3362000000.000000 | -21.180000 | 158734655.300000 | 93.089287 | -0.138596 | 1 | |
| NOV | National Oilwell Varco Inc. | Energy | Oil & Gas Equipment & Services | 33.490002 | -12.558739 | 1.952020 | 5.000000 | 49.000000 | -1456000000.000000 | -769000000.000000 | -1.990000 | 386432160.800000 | 93.089287 | 9.582536 | 1 | |
| OKE | ONEOK | Energy | Oil & Gas Exploration & Production | 24.660000 | -24.123077 | 3.560178 | 73.000000 | 6.000000 | -75150000.000000 | 244977000.000000 | 1.170000 | 209382051.300000 | 21.076923 | -8.025110 | 1 | |
| OXY | Occidental Petroleum | Energy | Oil & Gas Exploration & Production | 67.610001 | 0.865287 | 1.589520 | 32.000000 | 64.000000 | -588000000.000000 | -7829000000.000000 | -10.230000 | 765298142.700000 | 93.089287 | 3.345102 | 1 | |
| PWR | Quanta Services Inc. | Industrials | Industrial Conglomerates | 20.250000 | -16.632362 | 2.954291 | 10.000000 | 11.000000 | -61744000.000000 | 321824000.000000 | 1.590000 | 202405031.400000 | 12.735849 | 4.291894 | 1 | |
| RRC | Range Resources Corp. | Energy | Oil & Gas Exploration & Production | 24.610001 | -25.106512 | 3.712995 | 26.000000 | 0.000000 | 23000.000000 | -713685000.000000 | -4.290000 | 166360139.900000 | 93.089287 | 0.525090 | 1 | |
| SE | Spectra Energy Corp. | Energy | Oil & Gas Refining & Marketing & Transportation | 23.940001 | -9.898378 | 2.030786 | 3.000000 | 6.000000 | -2000000.000000 | 196000000.000000 | 0.290000 | 675862069.000000 | 82.551728 | -2.580408 | 1 | |
| SWN | Southwestern Energy | Energy | Oil & Gas Exploration & Production | 7.110000 | -44.798137 | 4.580042 | 200.000000 | 2.000000 | -38000000.000000 | -4556000000.000000 | -6.070000 | 402141680.400000 | 93.089287 | 1.273530 | 1 | |
| TDC | Teradata Corp. | Information Technology | Application Software | 26.420000 | -8.833678 | 2.730659 | 25.000000 | 88.000000 | 5000000.000000 | -214000000.000000 | -1.530000 | 139869281.000000 | 74.555557 | 4.068084 | 1 | |
| WMB | Williams Cos. | Energy | Oil & Gas Exploration & Production | 25.700001 | -30.988186 | 3.719560 | 9.000000 | 4.000000 | -140000000.000000 | -571000000.000000 | -0.760000 | 751315789.500000 | 93.089287 | -14.561121 | 1 | |
| XEC | Cimarex Energy | Energy | Oil & Gas Exploration & Production | 89.379997 | -14.403372 | 2.397940 | 86.000000 | 190.000000 | 373520000.000000 | -2408948000.000000 | -25.920000 | 92937808.640000 | 93.089287 | 7.186128 | 1 |
## Complete the code to print the companies in each cluster
for cl in df1["KM_segments"].unique():
print("In cluster {}, the following companies are present:".format(cl))
print(df1[df1["KM_segments"] == cl]["Security"].unique())
print()
In cluster 0, the following companies are present: ['American Airlines Group' 'AbbVie' 'Abbott Laboratories' 'Archer-Daniels-Midland Co' 'Ameren Corp' 'American Electric Power' 'AFLAC Inc' 'American International Group, Inc.' 'Apartment Investment & Mgmt' 'Assurant Inc' 'Arthur J. Gallagher & Co.' 'Akamai Technologies Inc' 'Albemarle Corp' 'Alaska Air Group Inc' 'Allstate Corp' 'Allegion' 'Applied Materials Inc' 'AMETEK Inc' 'Affiliated Managers Group Inc' 'Ameriprise Financial' 'American Tower Corp A' 'AutoNation Inc' 'Anthem Inc.' 'Aon plc' 'Amphenol Corp' 'Activision Blizzard' 'AvalonBay Communities, Inc.' 'American Water Works Company Inc' 'American Express Co' 'Boeing Company' 'Baxter International Inc.' 'BB&T Corporation' 'Bard (C.R.) Inc.' 'BIOGEN IDEC Inc.' 'The Bank of New York Mellon Corp.' 'Ball Corp' 'Bristol-Myers Squibb' 'Boston Scientific' 'BorgWarner' 'Boston Properties' 'Caterpillar Inc.' 'Chubb Limited' 'CBRE Group' 'Crown Castle International Corp.' 'Carnival Corp.' 'CF Industries Holdings Inc' 'Citizens Financial Group' 'Church & Dwight' 'C. H. Robinson Worldwide' 'Charter Communications' 'CIGNA Corp.' 'Cincinnati Financial' 'Colgate-Palmolive' 'Comerica Inc.' 'CME Group Inc.' 'Cummins Inc.' 'CMS Energy' 'Centene Corporation' 'CenterPoint Energy' 'Capital One Financial' 'The Cooper Companies' 'CSX Corp.' 'CenturyLink Inc' 'Cognizant Technology Solutions' 'Citrix Systems' 'CVS Health' 'Chevron Corp.' 'Dominion Resources' 'Delta Air Lines' 'Du Pont (E.I.)' 'Deere & Co.' 'Discover Financial Services' 'Quest Diagnostics' 'Danaher Corp.' 'The Walt Disney Company' 'Discovery Communications-A' 'Discovery Communications-C' 'Delphi Automotive' 'Digital Realty Trust' 'Dun & Bradstreet' 'Dover Corp.' 'Dr Pepper Snapple Group' 'Duke Energy' 'DaVita Inc.' 'Ecolab Inc.' 'Consolidated Edison' 'Equifax Inc.' "Edison Int'l" 'Eastman Chemical' 'Equity Residential' 'Eversource Energy' 'Essex Property Trust, Inc.' 'E*Trade' 'Eaton Corporation' 'Entergy Corp.' 'Exelon Corp.' "Expeditors Int'l" 'Expedia Inc.' 'Extra Space Storage' 'Fastenal Co' 'Fortune Brands Home & Security' 'FirstEnergy Corp' 'Fidelity National Information Services' 'Fiserv Inc' 'FLIR Systems' 'Fluor Corp.' 'Flowserve Corporation' 'FMC Corporation' 'Federal Realty Investment Trust' 'General Dynamics' 'General Growth Properties Inc.' 'Corning Inc.' 'General Motors' 'Genuine Parts' 'Garmin Ltd.' 'Goodyear Tire & Rubber' 'Grainger (W.W.) Inc.' 'Hasbro Inc.' 'Huntington Bancshares' 'HCA Holdings' 'Welltower Inc.' 'HCP Inc.' 'Hartford Financial Svc.Gp.' 'Harley-Davidson' "Honeywell Int'l Inc." 'HP Inc.' 'Hormel Foods Corp.' 'Henry Schein' 'Host Hotels & Resorts' 'The Hershey Company' 'Humana Inc.' 'International Business Machines' 'IDEXX Laboratories' 'Intl Flavors & Fragrances' 'International Paper' 'Interpublic Group' 'Iron Mountain Incorporated' 'Illinois Tool Works' 'Invesco Ltd.' 'J. B. Hunt Transport Services' 'Jacobs Engineering Group' 'Juniper Networks' 'Kimco Realty' 'Kimberly-Clark' 'Kansas City Southern' 'Leggett & Platt' 'Lennar Corp.' 'Laboratory Corp. of America Holding' 'LKQ Corporation' 'L-3 Communications Holdings' 'Lilly (Eli) & Co.' 'Lockheed Martin Corp.' 'Alliant Energy Corp' 'Leucadia National Corp.' 'Southwest Airlines' 'Level 3 Communications' 'LyondellBasell' 'Mastercard Inc.' 'Mid-America Apartments' 'Macerich' "Marriott Int'l." 'Masco Corp.' 'Mattel Inc.' "Moody's Corp" 'Mondelez International' 'MetLife Inc.' 'Mohawk Industries' 'Mead Johnson' 'McCormick & Co.' 'Martin Marietta Materials' 'Marsh & McLennan' '3M Company' 'Altria Group Inc' 'Marathon Petroleum' 'Merck & Co.' 'M&T Bank Corp.' 'Mettler Toledo' 'Mylan N.V.' 'Navient' 'NASDAQ OMX Group' 'NextEra Energy' 'Nielsen Holdings' 'Norfolk Southern Corp.' 'Northern Trust Corp.' 'Nucor Corp.' 'Newell Brands' 'Realty Income Corporation' 'Omnicom Group' "O'Reilly Automotive" "People's United Financial" 'Pitney-Bowes' 'PACCAR Inc.' 'PG&E Corp.' 'Public Serv. Enterprise Inc.' 'PepsiCo Inc.' 'Principal Financial Group' 'Procter & Gamble' 'Progressive Corp.' 'Pulte Homes Inc.' 'Philip Morris International' 'PNC Financial Services' 'Pentair Ltd.' 'Pinnacle West Capital' 'PPG Industries' 'PPL Corp.' 'Prudential Financial' 'Phillips 66' 'Praxair Inc.' 'PayPal' 'Ryder System' 'Royal Caribbean Cruises Ltd' 'Robert Half International' 'Roper Industries' 'Republic Services Inc' 'SCANA Corp' 'Charles Schwab Corporation' 'Sealed Air' 'Sherwin-Williams' 'SL Green Realty' 'Scripps Networks Interactive Inc.' 'Southern Co.' 'Simon Property Group Inc' 'S&P Global, Inc.' 'Stericycle Inc' 'Sempra Energy' 'SunTrust Banks' 'State Street Corp.' 'Synchrony Financial' 'Stryker Corp.' 'Molson Coors Brewing Company' 'Tegna, Inc.' 'Torchmark Corp.' 'Thermo Fisher Scientific' 'The Travelers Companies Inc.' 'Tractor Supply Company' 'Tyson Foods' 'Tesoro Petroleum Co.' 'Total System Services' 'Texas Instruments' 'Under Armour' 'United Continental Holdings' 'UDR Inc' 'Universal Health Services, Inc.' 'United Health Group Inc.' 'Unum Group' 'Union Pacific' 'United Parcel Service' 'United Technologies' 'Varian Medical Systems' 'Valero Energy' 'Vulcan Materials' 'Vornado Realty Trust' 'Verisk Analytics' 'Verisign Inc.' 'Ventas Inc' 'Wec Energy Group Inc' 'Whirlpool Corp.' 'Waste Management Inc.' 'Western Union Co' 'Weyerhaeuser Corp.' 'Wyndham Worldwide' 'Xcel Energy Inc' 'XL Capital' 'Dentsply Sirona' 'Xerox Corp.' 'Xylem Inc.' 'Yum! Brands Inc' 'Zimmer Biomet Holdings' 'Zions Bancorp' 'Zoetis'] In cluster 2, the following companies are present: ['Adobe Systems Inc' 'Analog Devices, Inc.' 'Alliance Data Systems' 'Amgen Inc' 'Broadcom' 'Bank of America Corp' 'Celgene Corp.' 'Chipotle Mexican Grill' 'eBay Inc.' 'Equinix' 'Edwards Lifesciences' 'Facebook' 'First Solar Inc' 'Frontier Communications' 'Halliburton Co.' "McDonald's Corp." 'Monster Beverage' 'Newmont Mining Corp. (Hldg. Co.)' 'Skyworks Solutions' 'TripAdvisor' 'Vertex Pharmaceuticals Inc' 'Waters Corporation' 'Wynn Resorts Ltd' 'Yahoo Inc.'] In cluster 3, the following companies are present: ['Alexion Pharmaceuticals' 'Amazon.com Inc' 'Intuitive Surgical Inc.' 'Netflix Inc.' 'Priceline.com Inc' 'Regeneron'] In cluster 4, the following companies are present: ['Apache Corporation' 'Chesapeake Energy'] In cluster 5, the following companies are present: ['Anadarko Petroleum Corp' 'Arconic Inc' 'Baker Hughes Inc' 'Cabot Oil & Gas' 'Concho Resources' 'Devon Energy Corp.' 'EOG Resources' 'EQT Corporation' 'Freeport-McMoran Cp & Gld' 'Hess Corporation' 'Hewlett Packard Enterprise' 'Kinder Morgan' 'The Mosaic Company' 'Marathon Oil Corp.' 'Murphy Oil' 'Noble Energy Inc' 'Newfield Exploration Co' 'National Oilwell Varco Inc.' 'ONEOK' 'Occidental Petroleum' 'Quanta Services Inc.' 'Range Resources Corp.' 'Spectra Energy Corp.' 'Southwestern Energy' 'Teradata Corp.' 'Williams Cos.' 'Cimarex Energy'] In cluster 1, the following companies are present: ['Citigroup Inc.' 'Ford Motor' 'Gilead Sciences' 'Intel Corp.' 'JPMorgan Chase & Co.' 'Coca Cola Company' 'Pfizer Inc.' 'AT&T Inc' 'Verizon Communications' 'Wells Fargo' 'Exxon Mobil Corp.']
df1.groupby(["KM_segments", "GICS Sector"])['Security'].count()
KM_segments GICS Sector
0 Consumer Discretionary 33
Consumer Staples 17
Energy 5
Financials 45
Health Care 30
Industrials 51
Information Technology 20
Materials 17
Real Estate 26
Telecommunications Services 2
Utilities 24
1 Consumer Discretionary 1
Consumer Staples 1
Energy 1
Financials 3
Health Care 2
Information Technology 1
Telecommunications Services 2
2 Consumer Discretionary 4
Consumer Staples 1
Energy 1
Financials 1
Health Care 5
Information Technology 9
Materials 1
Real Estate 1
Telecommunications Services 1
3 Consumer Discretionary 2
Health Care 3
Information Technology 1
4 Energy 2
5 Energy 21
Industrials 2
Information Technology 2
Materials 2
Name: Security, dtype: int64
plt.figure(figsize=(20, 20))
plt.suptitle("Boxplot of numerical variables for each cluster")
# selecting numerical columns
num_col = df.select_dtypes(include=np.number).columns.tolist()
for i, variable in enumerate(num_col):
plt.subplot(3, 4, i + 1)
sns.boxplot(data=df1, x="KM_segments", y=variable)
plt.tight_layout(pad=2.0)
-
hc_df = subset_scaled_df.copy()
Error: ValueError: The condensed distance matrix must contain only finite values.
Reference: ValueError: The condensed distance matrix must contain only finite values. in python
Quote: There are question marks in your input dataset
hc_df
| Current Price | Price Change | Volatility | ROE | Cash Ratio | Net Cash Flow | Net Income | Earnings Per Share | Estimated Shares Outstanding | P/E Ratio | P/B Ratio | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -0.393341 | 0.493950 | 0.272749 | 0.989601 | -0.210698 | -0.339355 | 1.554415 | 1.309399 | 0.107863 | -0.652487 | -0.506653 |
| 1 | -0.220837 | 0.355439 | 1.137045 | 0.937737 | 0.077269 | -0.002335 | 0.927628 | 0.056755 | 1.250274 | -0.311769 | -0.504205 |
| 2 | -0.367195 | 0.602479 | -0.427007 | -0.192905 | -0.033488 | 0.454058 | 0.744371 | 0.024831 | 1.098021 | -0.391502 | 0.094941 |
| 3 | 0.133567 | 0.825696 | -0.284802 | -0.317379 | 1.218059 | -0.152497 | -0.219816 | -0.230563 | -0.091622 | 0.947148 | 0.424333 |
| 4 | -0.260874 | -0.492636 | 0.296470 | -0.265515 | 2.237018 | 0.133564 | -0.202703 | -0.374982 | 1.978399 | 3.293307 | 0.199196 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 335 | -0.486181 | 0.901646 | 0.540121 | -0.255142 | 4.308162 | -0.559673 | -1.487784 | -1.127481 | 0.429111 | -0.082116 | 0.572194 |
| 336 | -0.289510 | -1.065766 | -0.079703 | 1.062211 | -0.476513 | 0.053235 | -0.051186 | 0.029391 | -0.167741 | -0.337154 | -0.152012 |
| 337 | 0.221913 | 0.439539 | -0.206067 | -0.400362 | 0.332009 | 0.164889 | -0.342467 | -0.303532 | -0.460058 | 2.233634 | -1.589390 |
| 338 | -0.547053 | -0.436811 | -0.097813 | -0.369243 | 0.320933 | -0.051022 | -0.301171 | -0.239684 | -0.377852 | -0.222714 | 0.118680 |
| 339 | -0.336453 | 1.051046 | 0.142671 | -0.078803 | -0.055639 | 0.111378 | -0.293666 | -0.318734 | -0.092942 | 0.854902 | 0.246754 |
340 rows × 11 columns
hc_df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 340 entries, 0 to 339 Data columns (total 11 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Current Price 340 non-null float64 1 Price Change 340 non-null float64 2 Volatility 340 non-null float64 3 ROE 340 non-null float64 4 Cash Ratio 340 non-null float64 5 Net Cash Flow 340 non-null float64 6 Net Income 340 non-null float64 7 Earnings Per Share 340 non-null float64 8 Estimated Shares Outstanding 340 non-null float64 9 P/E Ratio 340 non-null float64 10 P/B Ratio 340 non-null float64 dtypes: float64(11) memory usage: 29.3 KB
hc_df.isna()
| Current Price | Price Change | Volatility | ROE | Cash Ratio | Net Cash Flow | Net Income | Earnings Per Share | Estimated Shares Outstanding | P/E Ratio | P/B Ratio | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | False | False | False | False | False | False | False | False | False | False | False |
| 1 | False | False | False | False | False | False | False | False | False | False | False |
| 2 | False | False | False | False | False | False | False | False | False | False | False |
| 3 | False | False | False | False | False | False | False | False | False | False | False |
| 4 | False | False | False | False | False | False | False | False | False | False | False |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 335 | False | False | False | False | False | False | False | False | False | False | False |
| 336 | False | False | False | False | False | False | False | False | False | False | False |
| 337 | False | False | False | False | False | False | False | False | False | False | False |
| 338 | False | False | False | False | False | False | False | False | False | False | False |
| 339 | False | False | False | False | False | False | False | False | False | False | False |
340 rows × 11 columns
len(hc_df[col])
340
type(len(hc_df[col]))
int
hc_df.columns
Index(['Current Price', 'Price Change', 'Volatility', 'ROE', 'Cash Ratio',
'Net Cash Flow', 'Net Income', 'Earnings Per Share',
'Estimated Shares Outstanding', 'P/E Ratio', 'P/B Ratio'],
dtype='object')
count=0
for col in hc_df.columns:
for i in range(len(hc_df[col])):
if pd.isna(hc_df[col][i])==True:
count+=1
print("count of na's in col:", col, "is:")
print(count)
count=0
count of na's in col: Current Price is: 0 count of na's in col: Price Change is: 0 count of na's in col: Volatility is: 0 count of na's in col: ROE is: 0 count of na's in col: Cash Ratio is: 0 count of na's in col: Net Cash Flow is: 0 count of na's in col: Net Income is: 0 count of na's in col: Earnings Per Share is: 0 count of na's in col: Estimated Shares Outstanding is: 0 count of na's in col: P/E Ratio is: 0 count of na's in col: P/B Ratio is: 0
len(hc_df)
340
error: ValueError: The condensed distance matrix must contain only finite values.
workaround: remove jensenshannon distance_metric
(see next code cell below:)
# list of distance metrics
# https://docs.scipy.org/doc/scipy/reference/spatial.distance.html
# excluded `"jensenshannon", ` due to:
# error: ValueError: The condensed distance matrix must contain only finite values.
# https://github.com/SegataLab/hclust2/issues/3
# https://github.com/CoderSales/unsupervised-learning-clustering/issues/7
distance_metrics = ["braycurtis", "canberra", "chebyshev", "cityblock", "correlation", "cosine", "euclidean", "mahalanobis", "minkowski", "seuclidean", "sqeuclidean"] ## Complete the code to add distance metrics
# list of linkage methods
# https://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.hierarchy.linkage.html
# 'ward' excluded due to error:
# ValueError: Method 'ward' requires the distance metric to be Euclidean
linkage_methods = ["single", "complete", "average", "weighted"] ## Complete the code to add linkages
# distance_metrics = ["braycurtis", "canberra", "chebyshev", "cityblock", "correlation", "cosine", "euclidean", "jensenshannon", "mahalanobis", "minkowski", "seuclidean", "sqeuclidean"] ## Complete the code to add distance metrics
# linkage_methods = ["single", "complete", "average", "weighted"] ## Complete the code to add linkages
# gave error:
# ValueError: The condensed distance matrix must contain only finite values.
high_cophenet_corr = 0
high_dm_lm = [0, 0]
for dm in distance_metrics:
for lm in linkage_methods:
Z = linkage(hc_df, metric=dm, method=lm)
c, coph_dists = cophenet(Z, pdist(hc_df))
print(
"Cophenetic correlation for {} distance and {} linkage is {}.".format(
dm.capitalize(), lm, c
)
)
if high_cophenet_corr < c:
high_cophenet_corr = c
high_dm_lm[0] = dm
high_dm_lm[1] = lm
# printing the combination of distance metric and linkage method with the highest cophenetic correlation
print('*'*100)
print(
"Highest cophenetic correlation is {}, which is obtained with {} distance and {} linkage.".format(
high_cophenet_corr, high_dm_lm[0].capitalize(), high_dm_lm[1]
)
)
Cophenetic correlation for Braycurtis distance and single linkage is 0.5212071810658095. Cophenetic correlation for Braycurtis distance and complete linkage is 0.10240430426791322. Cophenetic correlation for Braycurtis distance and average linkage is 0.32627435525839565. Cophenetic correlation for Braycurtis distance and weighted linkage is 0.20642920853912072. Cophenetic correlation for Canberra distance and single linkage is 0.3154284565587037. Cophenetic correlation for Canberra distance and complete linkage is 0.12703984768895782. Cophenetic correlation for Canberra distance and average linkage is 0.28553906567883447. Cophenetic correlation for Canberra distance and weighted linkage is 0.1626519582860221. Cophenetic correlation for Chebyshev distance and single linkage is 0.9062538164750717. Cophenetic correlation for Chebyshev distance and complete linkage is 0.598891419111242. Cophenetic correlation for Chebyshev distance and average linkage is 0.9338265528030499. Cophenetic correlation for Chebyshev distance and weighted linkage is 0.9127355892367. Cophenetic correlation for Cityblock distance and single linkage is 0.9334186366528574. Cophenetic correlation for Cityblock distance and complete linkage is 0.7375328863205818. Cophenetic correlation for Cityblock distance and average linkage is 0.9302145048594667. Cophenetic correlation for Cityblock distance and weighted linkage is 0.731045513520281. Cophenetic correlation for Correlation distance and single linkage is 0.02168228519550047. Cophenetic correlation for Correlation distance and complete linkage is 0.13026598376888868. Cophenetic correlation for Correlation distance and average linkage is 0.23298451399122191. Cophenetic correlation for Correlation distance and weighted linkage is 0.1763411190029438. Cophenetic correlation for Cosine distance and single linkage is 0.1633637841831045. Cophenetic correlation for Cosine distance and complete linkage is 0.14558585821013348. Cophenetic correlation for Cosine distance and average linkage is 0.27565476401031014. Cophenetic correlation for Cosine distance and weighted linkage is 0.24177997570119666. Cophenetic correlation for Euclidean distance and single linkage is 0.9232271494002922. Cophenetic correlation for Euclidean distance and complete linkage is 0.7873280186580672. Cophenetic correlation for Euclidean distance and average linkage is 0.9422540609560814. Cophenetic correlation for Euclidean distance and weighted linkage is 0.8693784298129404. Cophenetic correlation for Mahalanobis distance and single linkage is 0.9259195530524591. Cophenetic correlation for Mahalanobis distance and complete linkage is 0.792530720285. Cophenetic correlation for Mahalanobis distance and average linkage is 0.9247324030159737. Cophenetic correlation for Mahalanobis distance and weighted linkage is 0.8708317490180426. Cophenetic correlation for Minkowski distance and single linkage is 0.9232271494002922. Cophenetic correlation for Minkowski distance and complete linkage is 0.7873280186580672. Cophenetic correlation for Minkowski distance and average linkage is 0.9422540609560814. Cophenetic correlation for Minkowski distance and weighted linkage is 0.8693784298129404. Cophenetic correlation for Seuclidean distance and single linkage is 0.923227149400292. Cophenetic correlation for Seuclidean distance and complete linkage is 0.787328018658067. Cophenetic correlation for Seuclidean distance and average linkage is 0.9422540609560816. Cophenetic correlation for Seuclidean distance and weighted linkage is 0.8693784298129404. Cophenetic correlation for Sqeuclidean distance and single linkage is 0.815145614432825. Cophenetic correlation for Sqeuclidean distance and complete linkage is 0.7784062963343734. Cophenetic correlation for Sqeuclidean distance and average linkage is 0.8984640112163556. Cophenetic correlation for Sqeuclidean distance and weighted linkage is 0.5506923587865349. **************************************************************************************************** Highest cophenetic correlation is 0.9422540609560816, which is obtained with Seuclidean distance and average linkage.
high_cophenet_corr
0.9422540609560816
high_dm_lm[0]
'seuclidean'
high_dm_lm[1]
'average'
Note on above 3 code cells
post main code cell for cophenet:
scope for variables:
high_cophenet_corrhigh_dm_lm[0]high_dm_lm[1]in print statement:
print(
"Highest cophenetic correlation is {}, which is obtained with {} distance and {} linkage.".format(
high_cophenet_corr, high_dm_lm[0].capitalize(), high_dm_lm[1]
)
)
must be code cell scope,
hence unavailable in new cells
hence 0's for last 3 cell outputs.
Let's explore different linkage methods with Euclidean distance only.
# list of linkage methods
linkage_methods = ["single", "complete", "average", "weighted"] ## Complete the code to add linkages
high_cophenet_corr = 0
high_dm_lm = [0, 0]
for lm in linkage_methods:
Z = linkage(hc_df, metric="euclidean", method=lm)
c, coph_dists = cophenet(Z, pdist(hc_df))
print("Cophenetic correlation for {} linkage is {}.".format(lm, c))
if high_cophenet_corr < c:
high_cophenet_corr = c
high_dm_lm[0] = "euclidean"
high_dm_lm[1] = lm
# printing the combination of distance metric and linkage method with the highest cophenetic correlation
print('*'*100)
print(
"Highest cophenetic correlation is {}, which is obtained with {} linkage.".format(
high_cophenet_corr, high_dm_lm[1]
)
)
Cophenetic correlation for single linkage is 0.9232271494002922. Cophenetic correlation for complete linkage is 0.7873280186580672. Cophenetic correlation for average linkage is 0.9422540609560814. Cophenetic correlation for weighted linkage is 0.8693784298129404. **************************************************************************************************** Highest cophenetic correlation is 0.9422540609560814, which is obtained with average linkage.
Let's view the dendrograms for the different linkage methods with Euclidean distance.
Estimated runtime around: ~17.9-28.1 s
# list of linkage methods
linkage_methods = ["single", "complete", "average", "centroid", "ward", "weighted"] ## Complete the code to add linkages
# lists to save results of cophenetic correlation calculation
compare_cols = ["Linkage", "Cophenetic Coefficient"]
compare = []
# to create a subplot image
fig, axs = plt.subplots(len(linkage_methods), 1, figsize=(15, 30))
# We will enumerate through the list of linkage methods above
# For each linkage method, we will plot the dendrogram and calculate the cophenetic correlation
for i, method in enumerate(linkage_methods):
Z = linkage(hc_df, metric="euclidean", method=method)
dendrogram(Z, ax=axs[i])
axs[i].set_title(f"Dendrogram ({method.capitalize()} Linkage)")
coph_corr, coph_dist = cophenet(Z, pdist(hc_df))
axs[i].annotate(
f"Cophenetic\nCorrelation\n{coph_corr:0.2f}",
(0.80, 0.80),
xycoords="axes fraction",
)
compare.append([method, coph_corr])
# create and print a dataframe to compare cophenetic correlations for different linkage methods
df_cc = pd.DataFrame(compare, columns=compare_cols)
df_cc = df_cc.sort_values(by="Cophenetic Coefficient")
df_cc
| Linkage | Cophenetic Coefficient | |
|---|---|---|
| 4 | ward | 0.710118 |
| 1 | complete | 0.787328 |
| 5 | weighted | 0.869378 |
| 0 | single | 0.923227 |
| 3 | centroid | 0.931401 |
| 2 | average | 0.942254 |
abbreviation: hc = hierarchical clustering
reference: Hierarchical clustering
HCmodel = AgglomerativeClustering(n_clusters=1, affinity='euclidean', linkage='ward') ## Complete the code to define the hierarchical clustering model
HCmodel.fit(hc_df)
AgglomerativeClustering(affinity='euclidean', n_clusters=1)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
AgglomerativeClustering(affinity='euclidean', n_clusters=1)
# creating a copy of the original data
df2 = df.copy()
# adding hierarchical cluster labels to the original and scaled dataframes
hc_df["HC_segments"] = HCmodel.labels_
df2["HC_segments"] = HCmodel.labels_
error: TypeError: Could not convert to numeric
df2.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 340 entries, 0 to 339 Data columns (total 16 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Ticker Symbol 340 non-null object 1 Security 340 non-null object 2 GICS Sector 340 non-null object 3 GICS Sub Industry 340 non-null object 4 Current Price 340 non-null float64 5 Price Change 340 non-null float64 6 Volatility 340 non-null float64 7 ROE 340 non-null int64 8 Cash Ratio 340 non-null int64 9 Net Cash Flow 340 non-null int64 10 Net Income 340 non-null int64 11 Earnings Per Share 340 non-null float64 12 Estimated Shares Outstanding 340 non-null float64 13 P/E Ratio 340 non-null float64 14 P/B Ratio 340 non-null float64 15 HC_segments 340 non-null int64 dtypes: float64(7), int64(5), object(4) memory usage: 42.6+ KB
hc_cluster_profile = df2.groupby(["HC_segments", "Ticker Symbol", "Security", "GICS Sector", "GICS Sub Industry"]).mean() ## Complete the code to groupby the cluster labels
groupby: ["HC_segments", "Ticker Symbol", "Security", "GICS Sector", "GICS Sub Industry"]
to fix bug:
no companies in groups / clusters when grouped by 'GICS Sub Industry',
(below, after large hc_cluster_profile output table below),
do not group by all object columns as well as by HC_segments
(that is Hierarchical Clustering segments)
object_list contains all object columns:
object_list
['Ticker Symbol', 'Security', 'GICS Sector', 'GICS Sub Industry']
# hc_cluster_profile["count_in_each_segment"] = (
# df2.groupby(["HC_segments", "Ticker Symbol", "Security", "GICS Sector", "GICS Sub Industry"])["Security"].count().values ## Complete the code to groupby the cluster labels
# )
hc_cluster_profile["count_in_each_segment"] = (
df2.groupby(["HC_segments", 'Ticker Symbol', 'Security', 'GICS Sector', "GICS Sub Industry"])["GICS Sub Industry"].count().values ## Complete the code to groupby the cluster labels
)
# from ChatGPT3:
# Requests:
# how to loop through dataframe which displays max highlighted in green so that output of all this stock exchange ticker data like pe ratio and pb ratio etc does not have to display like a thousand rows but rather only those rows with a max value in them
# regenerate code for all rows as there are more than pe and pb ratios
# exclude dtype obj cols
# adapt given that currently : hc_cluster_profile.style.highlight_max(color="lightgreen", axis=0)
# response test 1:
# function to highlight maximum values in numeric columns
# def highlight_max(s):
# if np.issubdtype(s.dtype, np.number):
# is_max = s == s.max()
# return ['background-color: green' if v else '' for v in is_max]
# else:
# return ['' for _ in s]
# # highlight maximum values in numeric columns only
# numeric_cols = hc_cluster_profile.select_dtypes(include=np.number).columns.tolist()
# hc_cluster_profile.style.apply(highlight_max, subset=numeric_cols)
# filter
# above failed as whole dataframe printed
# response test 2:
# function to highlight maximum values in numeric columns
def highlight_max(s):
if np.issubdtype(s.dtype, np.number):
is_max = s == s.max()
return ['background-color: green' if v else '' for v in is_max]
else:
return ['' for _ in s]
# highlight maximum values in numeric columns only
numeric_cols = hc_cluster_profile.select_dtypes(include=np.number).columns.tolist()
hc_cluster_profile.style.apply(highlight_max, subset=numeric_cols)
# filter DataFrame to show only rows with max value in any numeric column
max_values = hc_cluster_profile.loc[hc_cluster_profile[numeric_cols].idxmax().values]
# display filtered DataFrame
print("Rows with maximum values in any numeric column:")
print(max_values)
Rows with maximum values in any numeric column:
Current Price
HC_segments Ticker Symbol Security GICS Sector GICS Sub Industry
0 PCLN Priceline.com Inc Consumer Discretionary Internet & Direct Marketing Retail 1274.949951 \
FSLR First Solar Inc Information Technology Semiconductors 65.989998
SWN Southwestern Energy Energy Oil & Gas Exploration & Production 7.110000
APA Apache Corporation Energy Oil & Gas Exploration & Production 44.470001
FB Facebook Information Technology Internet Software & Services 104.660004
BAC Bank of America Corp Financials Banks 16.830000
JPM JPMorgan Chase & Co. Financials Banks 66.029999
PCLN Priceline.com Inc Consumer Discretionary Internet & Direct Marketing Retail 1274.949951
PFE Pfizer Inc. Health Care Pharmaceuticals 32.279999
AMZN Amazon.com Inc Consumer Discretionary Internet & Direct Marketing Retail 675.890015
ADS Alliance Data Systems Information Technology Data Processing & Outsourced Services 276.570007
AAL American Airlines Group Industrials Airlines 42.349998
Price Change
HC_segments Ticker Symbol Security GICS Sector GICS Sub Industry
0 PCLN Priceline.com Inc Consumer Discretionary Internet & Direct Marketing Retail 3.190527 \
FSLR First Solar Inc Information Technology Semiconductors 55.051683
SWN Southwestern Energy Energy Oil & Gas Exploration & Production -44.798137
APA Apache Corporation Energy Oil & Gas Exploration & Production 11.397804
FB Facebook Information Technology Internet Software & Services 16.224320
BAC Bank of America Corp Financials Banks 8.440722
JPM JPMorgan Chase & Co. Financials Banks 8.033377
PCLN Priceline.com Inc Consumer Discretionary Internet & Direct Marketing Retail 3.190527
PFE Pfizer Inc. Health Care Pharmaceuticals 3.130991
AMZN Amazon.com Inc Consumer Discretionary Internet & Direct Marketing Retail 32.268105
ADS Alliance Data Systems Information Technology Data Processing & Outsourced Services 6.189286
AAL American Airlines Group Industrials Airlines 9.999995
Volatility
HC_segments Ticker Symbol Security GICS Sector GICS Sub Industry
0 PCLN Priceline.com Inc Consumer Discretionary Internet & Direct Marketing Retail 1.268340 \
FSLR First Solar Inc Information Technology Semiconductors 2.075216
SWN Southwestern Energy Energy Oil & Gas Exploration & Production 4.580042
APA Apache Corporation Energy Oil & Gas Exploration & Production 2.405408
FB Facebook Information Technology Internet Software & Services 1.320606
BAC Bank of America Corp Financials Banks 1.418688
JPM JPMorgan Chase & Co. Financials Banks 1.130337
PCLN Priceline.com Inc Consumer Discretionary Internet & Direct Marketing Retail 1.268340
PFE Pfizer Inc. Health Care Pharmaceuticals 1.238748
AMZN Amazon.com Inc Consumer Discretionary Internet & Direct Marketing Retail 1.460386
ADS Alliance Data Systems Information Technology Data Processing & Outsourced Services 1.116976
AAL American Airlines Group Industrials Airlines 1.687151
ROE
HC_segments Ticker Symbol Security GICS Sector GICS Sub Industry
0 PCLN Priceline.com Inc Consumer Discretionary Internet & Direct Marketing Retail 29.0 \
FSLR First Solar Inc Information Technology Semiconductors 10.0
SWN Southwestern Energy Energy Oil & Gas Exploration & Production 200.0
APA Apache Corporation Energy Oil & Gas Exploration & Production 917.0
FB Facebook Information Technology Internet Software & Services 8.0
BAC Bank of America Corp Financials Banks 6.0
JPM JPMorgan Chase & Co. Financials Banks 10.0
PCLN Priceline.com Inc Consumer Discretionary Internet & Direct Marketing Retail 29.0
PFE Pfizer Inc. Health Care Pharmaceuticals 11.0
AMZN Amazon.com Inc Consumer Discretionary Internet & Direct Marketing Retail 4.0
ADS Alliance Data Systems Information Technology Data Processing & Outsourced Services 30.0
AAL American Airlines Group Industrials Airlines 135.0
Cash Ratio
HC_segments Ticker Symbol Security GICS Sector GICS Sub Industry
0 PCLN Priceline.com Inc Consumer Discretionary Internet & Direct Marketing Retail 184.0 \
FSLR First Solar Inc Information Technology Semiconductors 190.0
SWN Southwestern Energy Energy Oil & Gas Exploration & Production 2.0
APA Apache Corporation Energy Oil & Gas Exploration & Production 80.0
FB Facebook Information Technology Internet Software & Services 958.0
BAC Bank of America Corp Financials Banks 99.0
JPM JPMorgan Chase & Co. Financials Banks 99.0
PCLN Priceline.com Inc Consumer Discretionary Internet & Direct Marketing Retail 184.0
PFE Pfizer Inc. Health Care Pharmaceuticals 79.0
AMZN Amazon.com Inc Consumer Discretionary Internet & Direct Marketing Retail 58.0
ADS Alliance Data Systems Information Technology Data Processing & Outsourced Services 25.0
AAL American Airlines Group Industrials Airlines 51.0
Net Cash Flow
HC_segments Ticker Symbol Security GICS Sector GICS Sub Industry
0 PCLN Priceline.com Inc Consumer Discretionary Internet & Direct Marketing Retail -1.671386e+09 \
FSLR First Solar Inc Information Technology Semiconductors -3.552280e+08
SWN Southwestern Energy Energy Oil & Gas Exploration & Production -3.800000e+07
APA Apache Corporation Energy Oil & Gas Exploration & Production 6.980000e+08
FB Facebook Information Technology Internet Software & Services 5.920000e+08
BAC Bank of America Corp Financials Banks 2.076400e+10
JPM JPMorgan Chase & Co. Financials Banks -7.341000e+09
PCLN Priceline.com Inc Consumer Discretionary Internet & Direct Marketing Retail -1.671386e+09
PFE Pfizer Inc. Health Care Pharmaceuticals 2.980000e+08
AMZN Amazon.com Inc Consumer Discretionary Internet & Direct Marketing Retail 1.333000e+09
ADS Alliance Data Systems Information Technology Data Processing & Outsourced Services 9.088500e+07
AAL American Airlines Group Industrials Airlines -6.040000e+08
Net Income
HC_segments Ticker Symbol Security GICS Sector GICS Sub Industry
0 PCLN Priceline.com Inc Consumer Discretionary Internet & Direct Marketing Retail 2.551360e+09 \
FSLR First Solar Inc Information Technology Semiconductors 5.464210e+08
SWN Southwestern Energy Energy Oil & Gas Exploration & Production -4.556000e+09
APA Apache Corporation Energy Oil & Gas Exploration & Production -2.352800e+10
FB Facebook Information Technology Internet Software & Services 3.669000e+09
BAC Bank of America Corp Financials Banks 1.588800e+10
JPM JPMorgan Chase & Co. Financials Banks 2.444200e+10
PCLN Priceline.com Inc Consumer Discretionary Internet & Direct Marketing Retail 2.551360e+09
PFE Pfizer Inc. Health Care Pharmaceuticals 6.960000e+09
AMZN Amazon.com Inc Consumer Discretionary Internet & Direct Marketing Retail 5.960000e+08
ADS Alliance Data Systems Information Technology Data Processing & Outsourced Services 5.965410e+08
AAL American Airlines Group Industrials Airlines 7.610000e+09
Earnings Per Share
HC_segments Ticker Symbol Security GICS Sector GICS Sub Industry
0 PCLN Priceline.com Inc Consumer Discretionary Internet & Direct Marketing Retail 50.09 \
FSLR First Solar Inc Information Technology Semiconductors 5.42
SWN Southwestern Energy Energy Oil & Gas Exploration & Production -6.07
APA Apache Corporation Energy Oil & Gas Exploration & Production -61.20
FB Facebook Information Technology Internet Software & Services 1.31
BAC Bank of America Corp Financials Banks 4.18
JPM JPMorgan Chase & Co. Financials Banks 6.05
PCLN Priceline.com Inc Consumer Discretionary Internet & Direct Marketing Retail 50.09
PFE Pfizer Inc. Health Care Pharmaceuticals 1.13
AMZN Amazon.com Inc Consumer Discretionary Internet & Direct Marketing Retail 1.28
ADS Alliance Data Systems Information Technology Data Processing & Outsourced Services 8.91
AAL American Airlines Group Industrials Airlines 11.39
Estimated Shares Outstanding
HC_segments Ticker Symbol Security GICS Sector GICS Sub Industry
0 PCLN Priceline.com Inc Consumer Discretionary Internet & Direct Marketing Retail 5.093552e+07 \
FSLR First Solar Inc Information Technology Semiconductors 1.008157e+08
SWN Southwestern Energy Energy Oil & Gas Exploration & Production 4.021417e+08
APA Apache Corporation Energy Oil & Gas Exploration & Production 3.844444e+08
FB Facebook Information Technology Internet Software & Services 2.800763e+09
BAC Bank of America Corp Financials Banks 8.450695e+08
JPM JPMorgan Chase & Co. Financials Banks 4.040000e+09
PCLN Priceline.com Inc Consumer Discretionary Internet & Direct Marketing Retail 5.093552e+07
PFE Pfizer Inc. Health Care Pharmaceuticals 6.159292e+09
AMZN Amazon.com Inc Consumer Discretionary Internet & Direct Marketing Retail 4.656250e+08
ADS Alliance Data Systems Information Technology Data Processing & Outsourced Services 6.695185e+07
AAL American Airlines Group Industrials Airlines 6.681299e+08
P/E Ratio
HC_segments Ticker Symbol Security GICS Sector GICS Sub Industry
0 PCLN Priceline.com Inc Consumer Discretionary Internet & Direct Marketing Retail 25.453183 \
FSLR First Solar Inc Information Technology Semiconductors 12.175276
SWN Southwestern Energy Energy Oil & Gas Exploration & Production 93.089287
APA Apache Corporation Energy Oil & Gas Exploration & Production 93.089287
FB Facebook Information Technology Internet Software & Services 79.893133
BAC Bank of America Corp Financials Banks 13.004785
JPM JPMorgan Chase & Co. Financials Banks 10.914049
PCLN Priceline.com Inc Consumer Discretionary Internet & Direct Marketing Retail 25.453183
PFE Pfizer Inc. Health Care Pharmaceuticals 28.566371
AMZN Amazon.com Inc Consumer Discretionary Internet & Direct Marketing Retail 528.039074
ADS Alliance Data Systems Information Technology Data Processing & Outsourced Services 31.040405
AAL American Airlines Group Industrials Airlines 3.718174
P/B Ratio
HC_segments Ticker Symbol Security GICS Sector GICS Sub Industry
0 PCLN Priceline.com Inc Consumer Discretionary Internet & Direct Marketing Retail -1.052429 \
FSLR First Solar Inc Information Technology Semiconductors 22.563791
SWN Southwestern Energy Energy Oil & Gas Exploration & Production 1.273530
APA Apache Corporation Energy Oil & Gas Exploration & Production 4.970809
FB Facebook Information Technology Internet Software & Services 5.884467
BAC Bank of America Corp Financials Banks -0.938007
JPM JPMorgan Chase & Co. Financials Banks -1.886881
PCLN Priceline.com Inc Consumer Discretionary Internet & Direct Marketing Retail -1.052429
PFE Pfizer Inc. Health Care Pharmaceuticals -4.213309
AMZN Amazon.com Inc Consumer Discretionary Internet & Direct Marketing Retail 3.904430
ADS Alliance Data Systems Information Technology Data Processing & Outsourced Services 129.064585
AAL American Airlines Group Industrials Airlines -8.784219
count_in_each_segment
HC_segments Ticker Symbol Security GICS Sector GICS Sub Industry
0 PCLN Priceline.com Inc Consumer Discretionary Internet & Direct Marketing Retail 1
FSLR First Solar Inc Information Technology Semiconductors 1
SWN Southwestern Energy Energy Oil & Gas Exploration & Production 1
APA Apache Corporation Energy Oil & Gas Exploration & Production 1
FB Facebook Information Technology Internet Software & Services 1
BAC Bank of America Corp Financials Banks 1
JPM JPMorgan Chase & Co. Financials Banks 1
PCLN Priceline.com Inc Consumer Discretionary Internet & Direct Marketing Retail 1
PFE Pfizer Inc. Health Care Pharmaceuticals 1
AMZN Amazon.com Inc Consumer Discretionary Internet & Direct Marketing Retail 1
ADS Alliance Data Systems Information Technology Data Processing & Outsourced Services 1
AAL American Airlines Group Industrials Airlines 1
hc_cluster_profile.style.highlight_max(color="lightgreen", axis=0)
| Current Price | Price Change | Volatility | ROE | Cash Ratio | Net Cash Flow | Net Income | Earnings Per Share | Estimated Shares Outstanding | P/E Ratio | P/B Ratio | count_in_each_segment | |||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| HC_segments | Ticker Symbol | Security | GICS Sector | GICS Sub Industry | ||||||||||||
| 0 | AAL | American Airlines Group | Industrials | Airlines | 42.349998 | 9.999995 | 1.687151 | 135.000000 | 51.000000 | -604000000.000000 | 7610000000.000000 | 11.390000 | 668129938.500000 | 3.718174 | -8.784219 | 1 |
| ABBV | AbbVie | Health Care | Pharmaceuticals | 59.240002 | 8.339433 | 2.197887 | 130.000000 | 77.000000 | 51000000.000000 | 5144000000.000000 | 3.150000 | 1633015873.000000 | 18.806350 | -8.750068 | 1 | |
| ABT | Abbott Laboratories | Health Care | Health Care Equipment | 44.910000 | 11.301121 | 1.273646 | 21.000000 | 67.000000 | 938000000.000000 | 4423000000.000000 | 2.940000 | 1504421769.000000 | 15.275510 | -0.394171 | 1 | |
| ADBE | Adobe Systems Inc | Information Technology | Application Software | 93.940002 | 13.977195 | 1.357679 | 9.000000 | 180.000000 | -240840000.000000 | 629551000.000000 | 1.260000 | 499643650.800000 | 74.555557 | 4.199651 | 1 | |
| ADI | Analog Devices, Inc. | Information Technology | Semiconductors | 55.320000 | -1.827858 | 1.701169 | 14.000000 | 272.000000 | 315120000.000000 | 696878000.000000 | 0.310000 | 2247993548.000000 | 178.451613 | 1.059810 | 1 | |
| ADM | Archer-Daniels-Midland Co | Consumer Staples | Agricultural Products | 36.680000 | -12.017268 | 1.516493 | 10.000000 | 49.000000 | -189000000.000000 | 1849000000.000000 | 2.990000 | 618394648.800000 | 12.267559 | 7.496831 | 1 | |
| ADS | Alliance Data Systems | Information Technology | Data Processing & Outsourced Services | 276.570007 | 6.189286 | 1.116976 | 30.000000 | 25.000000 | 90885000.000000 | 596541000.000000 | 8.910000 | 66951851.850000 | 31.040405 | 129.064585 | 1 | |
| AEE | Ameren Corp | Utilities | MultiUtilities | 43.230000 | 2.174424 | 1.124186 | 9.000000 | 14.000000 | 287000000.000000 | 636000000.000000 | 2.600000 | 244615384.600000 | 16.626923 | -0.719497 | 1 | |
| AEP | American Electric Power | Utilities | Electric Utilities | 58.270000 | 2.371753 | 1.068485 | 11.000000 | 9.000000 | 13900000.000000 | 2052300000.000000 | 3.130000 | 421897810.200000 | 18.456543 | -3.022649 | 1 | |
| AFL | AFLAC Inc | Financials | Life & Health Insurance | 59.900002 | 3.027181 | 1.048295 | 14.000000 | 99.000000 | -308000000.000000 | 2533000000.000000 | 5.880000 | 430782312.900000 | 10.187075 | -1.883912 | 1 | |
| AIG | American International Group, Inc. | Financials | Property & Casualty Insurance | 61.970001 | 8.358108 | 1.106965 | 2.000000 | 99.000000 | -129000000.000000 | 2196000000.000000 | 1.690000 | 1299408284.000000 | 36.668640 | -4.327138 | 1 | |
| AIV | Apartment Investment & Mgmt | Real Estate | REITs | 40.029999 | 7.578608 | 1.163334 | 15.000000 | 47.000000 | 21818000.000000 | 248710000.000000 | 1.520000 | 163625000.000000 | 26.335526 | -1.269332 | 1 | |
| AIZ | Assurant Inc | Financials | Multi-line Insurance | 80.540001 | 1.897773 | 1.112604 | 3.000000 | 99.000000 | -30351000.000000 | 141555000.000000 | 2.080000 | 68055288.460000 | 38.721154 | -4.072615 | 1 | |
| AJG | Arthur J. Gallagher & Co. | Financials | Insurance Brokers | 40.939999 | -0.606943 | 1.052050 | 10.000000 | 99.000000 | 166000000.000000 | 356800000.000000 | 2.070000 | 172367149.800000 | 19.777777 | -9.855706 | 1 | |
| AKAM | Akamai Technologies Inc | Information Technology | Internet Software & Services | 52.630001 | -23.790903 | 1.384502 | 10.000000 | 225.000000 | 50823000.000000 | 321406000.000000 | 1.800000 | 178558888.900000 | 29.238889 | 4.282358 | 1 | |
| ALB | Albemarle Corp | Materials | Specialty Chemicals | 56.009998 | 26.461948 | 1.974323 | 10.000000 | 13.000000 | -2276034000.000000 | 334906000.000000 | 3.010000 | 111264451.800000 | 18.607973 | -13.649723 | 1 | |
| ALK | Alaska Air Group Inc | Industrials | Airlines | 80.510002 | 2.066436 | 1.773431 | 35.000000 | 74.000000 | -34000000.000000 | 848000000.000000 | 6.610000 | 128290469.000000 | 12.180031 | -1.114658 | 1 | |
| ALL | Allstate Corp | Financials | Property & Casualty Insurance | 62.090000 | 6.592275 | 1.053266 | 11.000000 | 99.000000 | -162000000.000000 | 2171000000.000000 | 5.120000 | 424023437.500000 | 12.126953 | -4.327138 | 1 | |
| ALLE | Allegion | Industrials | Building Products | 65.919998 | 13.753230 | 1.283795 | 601.000000 | 45.000000 | -90800000.000000 | 153900000.000000 | 1.600000 | 96187500.000000 | 41.199999 | -0.877453 | 1 | |
| ALXN | Alexion Pharmaceuticals | Health Care | Biotechnology | 190.750000 | 22.338380 | 2.022921 | 2.000000 | 195.000000 | 66000000.000000 | 144000000.000000 | 0.680000 | 211764705.900000 | 280.514706 | -14.171389 | 1 | |
| AMAT | Applied Materials Inc | Information Technology | Semiconductor Equipment | 18.670000 | 26.834239 | 1.460030 | 18.000000 | 131.000000 | 1795000000.000000 | 1377000000.000000 | 1.130000 | 1218584071.000000 | 16.522124 | 3.857756 | 1 | |
| AME | AMETEK Inc | Industrials | Electrical Components & Equipment | 53.590000 | 2.212474 | 1.089266 | 18.000000 | 37.000000 | 3390000.000000 | 590859000.000000 | 2.460000 | 240186585.400000 | 21.784553 | -4.490342 | 1 | |
| AMG | Affiliated Managers Group Inc | Financials | Asset Management & Custody Banks | 159.759995 | -6.611335 | 2.093065 | 18.000000 | 67.000000 | 13200000.000000 | 516000000.000000 | 9.490000 | 54373024.240000 | 16.834562 | -31.015380 | 1 | |
| AMGN | Amgen Inc | Health Care | Biotechnology | 162.330002 | 17.163478 | 1.630259 | 25.000000 | 362.000000 | 413000000.000000 | 6939000000.000000 | 9.150000 | 758360655.700000 | 17.740984 | 24.012322 | 1 | |
| AMP | Ameriprise Financial | Financials | Asset Management & Custody Banks | 106.419998 | -2.420686 | 1.222260 | 22.000000 | 67.000000 | -281000000.000000 | 1562000000.000000 | 8.600000 | 181627907.000000 | 12.374418 | -13.398380 | 1 | |
| AMT | American Tower Corp A | Real Estate | Specialized REITs | 96.949997 | 10.233087 | 1.165804 | 10.000000 | 39.000000 | 7194000.000000 | 685074000.000000 | 1.420000 | 482446478.900000 | 68.274646 | -20.813577 | 1 | |
| AMZN | Amazon.com Inc | Consumer Discretionary | Internet & Direct Marketing Retail | 675.890015 | 32.268105 | 1.460386 | 4.000000 | 58.000000 | 1333000000.000000 | 596000000.000000 | 1.280000 | 465625000.000000 | 528.039074 | 3.904430 | 1 | |
| AN | AutoNation Inc | Consumer Discretionary | Specialty Stores | 59.660000 | 2.350316 | 1.480914 | 19.000000 | 1.000000 | -1300000.000000 | 442600000.000000 | 3.930000 | 112620865.100000 | 15.180662 | -7.970104 | 1 | |
| ANTM | Anthem Inc. | Health Care | Managed Health Care | 139.440002 | -0.620053 | 1.511654 | 11.000000 | 70.000000 | -38200000.000000 | 2560000000.000000 | 9.730000 | 263103802.700000 | 14.330935 | -31.006773 | 1 | |
| AON | Aon plc | Financials | Insurance Brokers | 92.209999 | 3.910301 | 1.105032 | 23.000000 | 99.000000 | 10000000.000000 | 1385000000.000000 | 4.930000 | 280933062.900000 | 18.703854 | -7.759856 | 1 | |
| APA | Apache Corporation | Energy | Oil & Gas Exploration & Production | 44.470001 | 11.397804 | 2.405408 | 917.000000 | 80.000000 | 698000000.000000 | -23528000000.000000 | -61.200000 | 384444444.400000 | 93.089287 | 4.970809 | 1 | |
| APC | Anadarko Petroleum Corp | Energy | Oil & Gas Exploration & Production | 48.580002 | -20.802083 | 2.435165 | 52.000000 | 22.000000 | -6430000000.000000 | -6692000000.000000 | -13.180000 | 507738998.500000 | 93.089287 | -12.860938 | 1 | |
| APH | Amphenol Corp | Information Technology | Electronic Components | 52.230000 | 2.693667 | 1.007762 | 24.000000 | 175.000000 | 768300000.000000 | 763500000.000000 | 2.470000 | 309109311.700000 | 21.145749 | 8.202923 | 1 | |
| ARNC | Arconic Inc | Industrials | Aerospace & Defense | 7.398807 | 1.647784 | 2.592065 | 3.000000 | 37.000000 | 42000000.000000 | -322000000.000000 | -0.310000 | 1038709677.000000 | 18.687607 | 2.639814 | 1 | |
| ATVI | Activision Blizzard | Information Technology | Home Entertainment Software | 38.709999 | 23.319529 | 1.886335 | 11.000000 | 70.000000 | -3025000000.000000 | 892000000.000000 | 1.210000 | 737190082.600000 | 31.991735 | 0.290291 | 1 | |
| AVB | AvalonBay Communities, Inc. | Real Estate | Residential REITs | 184.130005 | 4.857630 | 1.132875 | 8.000000 | 47.000000 | -108953000.000000 | 741733000.000000 | 5.540000 | 133886823.100000 | 33.236463 | -3.089477 | 1 | |
| AVGO | Broadcom | Information Technology | Semiconductors | 145.149994 | 17.902683 | 1.847180 | 29.000000 | 163.000000 | 218000000.000000 | 1364000000.000000 | 2.860000 | 1044055944.000000 | 18.249407 | 3.954975 | 1 | |
| AWK | American Water Works Company Inc | Utilities | Water Utilities | 59.750000 | 8.596874 | 1.171525 | 9.000000 | 4.000000 | 22000000.000000 | 476000000.000000 | 2.660000 | 178947368.400000 | 22.462406 | -4.895294 | 1 | |
| AXP | American Express Co | Financials | Consumer Finance | 69.550003 | -6.216290 | 0.900066 | 25.000000 | 99.000000 | 474000000.000000 | 5163000000.000000 | 3.900000 | 506660363.000000 | 10.263506 | -0.609074 | 1 | |
| BA | Boeing Company | Industrials | Aerospace & Defense | 144.589996 | 10.105078 | 1.155905 | 82.000000 | 24.000000 | -431000000.000000 | 5176000000.000000 | 7.520000 | 688297872.300000 | 19.227393 | 22.032612 | 1 | |
| BAC | Bank of America Corp | Financials | Banks | 16.830000 | 8.440722 | 1.418688 | 6.000000 | 99.000000 | 20764000000.000000 | 15888000000.000000 | 4.180000 | 845069512.200000 | 13.004785 | -0.938007 | 1 | |
| BAX | Baxter International Inc. | Health Care | Health Care Equipment | 38.150002 | 16.702365 | 1.204526 | 11.000000 | 128.000000 | -712000000.000000 | 968000000.000000 | 1.780000 | 543820224.700000 | 21.432585 | 8.637045 | 1 | |
| BBT | BB&T Corporation | Financials | Banks | 37.810001 | 5.940045 | 1.077678 | 8.000000 | 99.000000 | 1386000000.000000 | 2084000000.000000 | 2.590000 | 804633204.600000 | 14.598456 | -0.852562 | 1 | |
| BCR | Bard (C.R.) Inc. | Health Care | Health Care Equipment | 189.440002 | 1.549182 | 1.394436 | 9.000000 | 82.000000 | -9600000.000000 | 135400000.000000 | 1.800000 | 75222222.220000 | 105.244446 | -4.127770 | 1 | |
| BHI | Baker Hughes Inc | Energy | Oil & Gas Equipment & Services | 46.150002 | -12.312367 | 2.559553 | 12.000000 | 84.000000 | 584000000.000000 | -1967000000.000000 | -4.490000 | 438084632.500000 | 93.089287 | 13.490544 | 1 | |
| BIIB | BIOGEN IDEC Inc. | Health Care | Biotechnology | 306.350006 | 4.917982 | 1.825994 | 38.000000 | 133.000000 | 148900000.000000 | 3547000000.000000 | 15.380000 | 230624187.300000 | 19.918726 | 0.162602 | 1 | |
| BK | The Bank of New York Mellon Corp. | Financials | Banks | 41.220001 | 5.422003 | 1.201660 | 8.000000 | 99.000000 | -433000000.000000 | 3158000000.000000 | 2.730000 | 1156776557.000000 | 15.098901 | -3.321298 | 1 | |
| BLL | Ball Corp | Materials | Metal & Glass Containers | 72.730003 | 16.535816 | 1.386684 | 22.000000 | 10.000000 | 32600000.000000 | 280900000.000000 | 2.050000 | 137024390.200000 | 35.478050 | -3.895657 | 1 | |
| BMY | Bristol-Myers Squibb | Health Care | Health Care Distributors | 68.790001 | 16.081680 | 1.498872 | 11.000000 | 53.000000 | -3186000000.000000 | 1565000000.000000 | 0.940000 | 1664893617.000000 | 73.180852 | 0.588026 | 1 | |
| BSX | Boston Scientific | Health Care | Health Care Equipment | 18.440001 | 11.757582 | 1.491764 | 4.000000 | 13.000000 | -268000000.000000 | -239000000.000000 | -0.180000 | 1327777778.000000 | 31.468962 | -3.880921 | 1 | |
| BWA | BorgWarner | Consumer Discretionary | Auto Parts & Equipment | 43.230000 | 3.470563 | 2.058769 | 17.000000 | 25.000000 | -220100000.000000 | 609700000.000000 | 2.720000 | 224154411.800000 | 15.893382 | 1.044815 | 1 | |
| BXP | Boston Properties | Real Estate | REITs | 127.540001 | 7.203497 | 1.089469 | 10.000000 | 47.000000 | -1039361000.000000 | 583106000.000000 | 3.790000 | 153853825.900000 | 33.651715 | -1.269332 | 1 | |
| C | Citigroup Inc. | Financials | Banks | 51.750000 | 4.714695 | 1.261984 | 8.000000 | 99.000000 | -11208000000.000000 | 17242000000.000000 | 5.410000 | 3187060998.000000 | 9.565619 | -1.167533 | 1 | |
| CAT | Caterpillar Inc. | Industrials | Construction & Farm Machinery & Heavy Trucks | 67.959999 | 3.550209 | 1.493553 | 17.000000 | 25.000000 | -881000000.000000 | 2512000000.000000 | 3.540000 | 709604519.800000 | 19.197740 | 6.264053 | 1 | |
| CB | Chubb Limited | Financials | Property & Casualty Insurance | 116.849998 | 13.193834 | 0.944847 | 10.000000 | 99.000000 | 1120000000.000000 | 2834000000.000000 | 8.710000 | 325373134.300000 | 13.415614 | -17.466101 | 1 | |
| CBG | CBRE Group | Real Estate | Real Estate Services | 34.580002 | 8.197757 | 1.297857 | 20.000000 | 12.000000 | -200481000.000000 | 547132000.000000 | 1.640000 | 333617073.200000 | 21.085367 | -3.415302 | 1 | |
| CCI | Crown Castle International Corp. | Real Estate | REITs | 86.449997 | 9.569068 | 0.960191 | 21.000000 | 36.000000 | 3190000.000000 | 1520992000.000000 | 4.440000 | 342565765.800000 | 19.470720 | -10.666679 | 1 | |
| CCL | Carnival Corp. | Consumer Discretionary | Hotels, Resorts & Cruise Lines | 54.480000 | 8.938217 | 1.347239 | 7.000000 | 20.000000 | 1064000000.000000 | 1757000000.000000 | 2.260000 | 777433628.300000 | 24.106195 | -7.477166 | 1 | |
| CELG | Celgene Corp. | Health Care | Biotechnology | 119.760002 | 8.448793 | 2.000828 | 27.000000 | 333.000000 | 758700000.000000 | 1602000000.000000 | 2.020000 | 793069306.900000 | 59.287130 | -4.320051 | 1 | |
| CF | CF Industries Holdings Inc | Materials | Fertilizers & Agricultural Chemicals | 40.810001 | -9.250611 | 2.368186 | 16.000000 | 25.000000 | -1710600000.000000 | 664900000.000000 | 2.970000 | 223872053.900000 | 13.740741 | -0.393528 | 1 | |
| CFG | Citizens Financial Group | Financials | Regional Banks | 26.190001 | 10.273688 | 1.189236 | 4.000000 | 99.000000 | -191000000.000000 | 840000000.000000 | 1.550000 | 541935483.900000 | 16.896775 | -0.063096 | 1 | |
| CHD | Church & Dwight | Consumer Staples | Household Products | 42.439999 | 1.047615 | 0.929026 | 20.000000 | 38.000000 | -93000000.000000 | 410400000.000000 | 3.130000 | 131118210.900000 | 13.559105 | -9.428134 | 1 | |
| CHK | Chesapeake Energy | Energy | Integrated Oil & Gas | 4.500000 | -38.101788 | 4.559815 | 687.000000 | 22.000000 | -3283000000.000000 | -14685000000.000000 | -22.430000 | 654703522.100000 | 28.407929 | -1.840528 | 1 | |
| CHRW | C. H. Robinson Worldwide | Industrials | Air Freight & Logistics | 62.020000 | -9.008221 | 1.185473 | 44.000000 | 12.000000 | 39289000.000000 | 509699000.000000 | 3.520000 | 144800852.300000 | 17.619318 | 1.117804 | 1 | |
| CHTR | Charter Communications | Consumer Discretionary | Cable & Satellite | 183.100006 | 3.598507 | 1.697942 | 589.000000 | 0.000000 | 2000000.000000 | -271000000.000000 | -2.430000 | 111522633.700000 | 20.819876 | -76.119077 | 1 | |
| CI | CIGNA Corp. | Health Care | Managed Health Care | 146.330002 | 8.682415 | 1.588398 | 17.000000 | 70.000000 | 548000000.000000 | 2094000000.000000 | 8.170000 | 256303549.600000 | 17.910649 | -8.805281 | 1 | |
| CINF | Cincinnati Financial | Financials | Property & Casualty Insurance | 59.169998 | 9.777358 | 0.935812 | 10.000000 | 99.000000 | -47000000.000000 | 634000000.000000 | 3.870000 | 163824289.400000 | 15.289405 | -4.327138 | 1 | |
| CL | Colgate-Palmolive | Consumer Staples | Household Products | 66.620003 | 4.781379 | 0.895471 | 463.000000 | 27.000000 | -119000000.000000 | 1384000000.000000 | 1.530000 | 904575163.400000 | 43.542486 | -0.548324 | 1 | |
| CMA | Comerica Inc. | Financials | Regional Banks | 41.830002 | 1.900129 | 1.557655 | 7.000000 | 99.000000 | 76000000.000000 | 521000000.000000 | 2.930000 | 177815699.700000 | 14.276451 | -0.063096 | 1 | |
| CME | CME Group Inc. | Financials | Financial Exchanges & Data | 90.599998 | -2.402245 | 1.323348 | 6.000000 | 99.000000 | 326500000.000000 | 1247000000.000000 | 3.710000 | 336118598.400000 | 24.420485 | -58.649536 | 1 | |
| CMG | Chipotle Mexican Grill | Consumer Discretionary | Restaurants | 479.850006 | -33.131268 | 2.474002 | 22.000000 | 237.000000 | -171460000.000000 | 475602000.000000 | 15.300000 | 31085098.040000 | 31.362745 | 17.201329 | 1 | |
| CMI | Cummins Inc. | Industrials | Industrial Machinery | 88.010002 | -18.884791 | 1.472364 | 19.000000 | 48.000000 | -590000000.000000 | 1399000000.000000 | 7.860000 | 177989821.900000 | 11.197201 | 21.439428 | 1 | |
| CMS | CMS Energy | Utilities | MultiUtilities | 36.080002 | 1.949712 | 1.037844 | 14.000000 | 12.000000 | 39000000.000000 | 537000000.000000 | 1.900000 | 282631578.900000 | 18.989475 | 0.063687 | 1 | |
| CNC | Centene Corporation | Health Care | Managed Health Care | 65.809998 | 21.712591 | 2.298696 | 16.000000 | 70.000000 | 150000000.000000 | 355000000.000000 | 2.990000 | 118729097.000000 | 22.010033 | -1.305493 | 1 | |
| CNP | CenterPoint Energy | Utilities | MultiUtilities | 18.360001 | 1.436470 | 1.389867 | 20.000000 | 43.000000 | -34000000.000000 | -692000000.000000 | -1.610000 | 429813664.600000 | 17.313076 | 0.516503 | 1 | |
| COF | Capital One Financial | Financials | Consumer Finance | 72.180000 | -0.619575 | 1.364592 | 9.000000 | 99.000000 | 781000000.000000 | 4050000000.000000 | 7.150000 | 566433566.400000 | 10.095105 | -0.725643 | 1 | |
| COG | Cabot Oil & Gas | Energy | Oil & Gas Exploration & Production | 17.690001 | -20.099360 | 3.055818 | 6.000000 | 0.000000 | -20440000.000000 | -113891000.000000 | -0.280000 | 406753571.400000 | 93.089287 | -0.223147 | 1 | |
| COO | The Cooper Companies | Health Care | Health Care Supplies | 134.199997 | -9.672215 | 1.555057 | 8.000000 | 3.000000 | -8796000.000000 | 203523000.000000 | 1.790000 | 140335195.500000 | 33.994412 | 0.855096 | 1 | |
| CSX | CSX Corp. | Industrials | Railroads | 25.950001 | -4.349421 | 1.626219 | 17.000000 | 74.000000 | -41000000.000000 | 1968000000.000000 | 2.000000 | 984000000.000000 | 12.975001 | 0.902439 | 1 | |
| CTL | CenturyLink Inc | Telecommunications Services | Integrated Telecommunications Services | 25.160000 | 0.159232 | 1.522194 | 6.000000 | 3.000000 | -2000000.000000 | 878000000.000000 | 1.580000 | 555696202.500000 | 15.924051 | -13.383212 | 1 | |
| CTSH | Cognizant Technology Solutions | Information Technology | IT Consulting & Other Services | 60.020000 | -4.654489 | 1.338123 | 17.000000 | 182.000000 | 115100000.000000 | 1623600000.000000 | 2.670000 | 608089887.600000 | 22.479401 | 7.121644 | 1 | |
| CTXS | Citrix Systems | Information Technology | Internet Software & Services | 75.650002 | 9.021477 | 1.968864 | 16.000000 | 52.000000 | 108369000.000000 | 319361000.000000 | 2.010000 | 158886069.700000 | 37.636817 | -1.765013 | 1 | |
| CVS | CVS Health | Consumer Staples | Drug Retail | 97.769997 | 1.326561 | 1.487367 | 14.000000 | 11.000000 | -22000000.000000 | 5237000000.000000 | 4.660000 | 1123819742.000000 | 20.980686 | -7.019809 | 1 | |
| CVX | Chevron Corp. | Energy | Integrated Oil & Gas | 89.959999 | 12.844955 | 1.750655 | 3.000000 | 43.000000 | -1763000000.000000 | 4587000000.000000 | 2.460000 | 1864634146.000000 | 36.569105 | 4.763937 | 1 | |
| CXO | Concho Resources | Energy | Oil & Gas Exploration & Production | 92.860001 | -2.744032 | 2.692546 | 1.000000 | 38.000000 | 228529000.000000 | 65900000.000000 | 0.540000 | 122037037.000000 | 171.962965 | 5.673991 | 1 | |
| D | Dominion Resources | Utilities | Electric Utilities | 67.639999 | -3.988642 | 0.889931 | 15.000000 | 8.000000 | 289000000.000000 | 1899000000.000000 | 3.210000 | 591588785.000000 | 21.071651 | -7.604945 | 1 | |
| DAL | Delta Air Lines | Industrials | Airlines | 50.689999 | 13.375084 | 1.444219 | 42.000000 | 31.000000 | -116000000.000000 | 4526000000.000000 | 5.680000 | 796830985.900000 | 8.924296 | -16.730022 | 1 | |
| DD | Du Pont (E.I.) | Materials | Diversified Chemicals | 66.599998 | 37.489677 | 1.577881 | 20.000000 | 60.000000 | -1610000000.000000 | 1953000000.000000 | 2.170000 | 900000000.000000 | 30.691243 | 3.252222 | 1 | |
| DE | Deere & Co. | Industrials | Construction & Farm Machinery & Heavy Trucks | 76.269997 | 3.952561 | 1.551946 | 29.000000 | 22.000000 | 375200000.000000 | 1940000000.000000 | 4.030000 | 532235888.200000 | 14.842233 | 6.277287 | 1 | |
| DFS | Discover Financial Services | Financials | Consumer Finance | 53.619999 | 3.653584 | 1.159897 | 20.000000 | 99.000000 | 2288000000.000000 | 2297000000.000000 | 5.140000 | 446887159.500000 | 10.431906 | -0.375934 | 1 | |
| DGX | Quest Diagnostics | Health Care | Health Care Facilities | 71.139999 | 15.674795 | 1.381490 | 15.000000 | 11.000000 | -59000000.000000 | 709000000.000000 | 4.920000 | 144105691.100000 | 14.459349 | -4.552214 | 1 | |
| DHR | Danaher Corp. | Industrials | Industrial Conglomerates | 70.416985 | 8.924595 | 1.191466 | 14.000000 | 13.000000 | -2214800000.000000 | 3357400000.000000 | 4.810000 | 698004158.000000 | 14.639706 | -13.759230 | 1 | |
| DIS | The Walt Disney Company | Consumer Discretionary | Broadcasting & Cable TV | 105.080002 | 2.049141 | 1.188454 | 19.000000 | 26.000000 | 848000000.000000 | 8382000000.000000 | 4.950000 | 1693333333.000000 | 21.228283 | -3.985039 | 1 | |
| DISCA | Discovery Communications-A | Consumer Discretionary | Cable & Satellite | 26.680000 | 2.026769 | 1.689235 | 19.000000 | 25.000000 | 23000000.000000 | 1034000000.000000 | -2.430000 | 111522633.700000 | 20.819876 | -76.119077 | 1 | |
| DISCK | Discovery Communications-C | Consumer Discretionary | Cable & Satellite | 25.219999 | 3.572891 | 1.812144 | 19.000000 | 25.000000 | 23000000.000000 | 1034000000.000000 | -2.430000 | 111522633.700000 | 20.819876 | -76.119077 | 1 | |
| DLPH | Delphi Automotive | Consumer Discretionary | Auto Parts & Equipment | 85.730003 | 12.109326 | 1.440884 | 64.000000 | 14.000000 | -325000000.000000 | 1450000000.000000 | 5.080000 | 285433070.900000 | 16.875985 | -0.662152 | 1 | |
| DLR | Digital Realty Trust | Real Estate | Specialized REITs | 75.620003 | 15.573900 | 1.070406 | 7.000000 | 39.000000 | 22239000.000000 | 296689000.000000 | 1.560000 | 190185256.400000 | 48.474361 | -7.489313 | 1 | |
| DNB | Dun & Bradstreet | Industrials | Research & Consulting Services | 103.930000 | -1.188439 | 1.337924 | 15.000000 | 38.000000 | 46300000.000000 | 168800000.000000 | 4.680000 | 36068376.070000 | 22.207265 | -11.877441 | 1 | |
| DOV | Dover Corp. | Industrials | Industrial Machinery | 61.310001 | 6.979585 | 1.507569 | 24.000000 | 26.000000 | -319396000.000000 | 869829000.000000 | 5.520000 | 157577717.400000 | 11.106884 | -2.293440 | 1 | |
| DPS | Dr Pepper Snapple Group | Consumer Staples | Soft Drinks | 93.199997 | 18.049399 | 1.150797 | 35.000000 | 58.000000 | 683000000.000000 | 764000000.000000 | 4.000000 | 191000000.000000 | 23.299999 | -12.717277 | 1 | |
| DUK | Duke Energy | Utilities | Electric Utilities | 71.389999 | -0.833448 | 1.096727 | 7.000000 | 8.000000 | -1179000000.000000 | 2816000000.000000 | 4.050000 | 695308642.000000 | 17.627160 | -4.426811 | 1 | |
| DVA | DaVita Inc. | Health Care | Health Care Facilities | 69.709999 | -3.622291 | 1.211643 | 6.000000 | 79.000000 | 533875000.000000 | 269732000.000000 | 1.270000 | 212387401.600000 | 54.889763 | 1.962527 | 1 | |
| DVN | Devon Energy Corp. | Energy | Oil & Gas Exploration & Production | 32.000000 | -15.478079 | 2.923698 | 205.000000 | 70.000000 | 830000000.000000 | -14454000000.000000 | -35.550000 | 406582278.500000 | 93.089287 | 1.785616 | 1 | |
| EBAY | eBay Inc. | Information Technology | Internet Software & Services | 27.480000 | 12.163265 | 1.409302 | 26.000000 | 271.000000 | -4496000000.000000 | 1725000000.000000 | 1.430000 | 1206293706.000000 | 19.216783 | 4.601699 | 1 | |
| ECL | Ecolab Inc. | Materials | Specialty Chemicals | 114.379997 | 3.783684 | 1.078516 | 15.000000 | 2.000000 | -116800000.000000 | 1002100000.000000 | 3.380000 | 296479289.900000 | 33.840236 | -14.928867 | 1 | |
| ED | Consolidated Edison | Utilities | Electric Utilities | 64.269997 | -3.974306 | 1.068002 | 9.000000 | 20.000000 | 249000000.000000 | 1193000000.000000 | 4.070000 | 293120393.100000 | 15.791154 | -3.022649 | 1 | |
| EFX | Equifax Inc. | Industrials | Research & Consulting Services | 111.370003 | 14.531063 | 1.081040 | 19.000000 | 15.000000 | -35000000.000000 | 429100000.000000 | 3.610000 | 118864265.900000 | 30.850416 | -8.116821 | 1 | |
| EIX | Edison Int'l | Utilities | Electric Utilities | 59.209999 | -6.135071 | 0.927260 | 10.000000 | 3.000000 | 29000000.000000 | 1117000000.000000 | 3.130000 | 356869009.600000 | 18.916933 | -6.369284 | 1 | |
| EMN | Eastman Chemical | Materials | Diversified Chemicals | 67.510002 | 3.654238 | 1.404508 | 22.000000 | 14.000000 | 79000000.000000 | 848000000.000000 | 5.710000 | 148511383.500000 | 11.823118 | -12.308821 | 1 | |
| EOG | EOG Resources | Energy | Oil & Gas Exploration & Production | 70.790001 | -4.078593 | 1.941104 | 35.000000 | 39.000000 | -1368707000.000000 | -4524515000.000000 | -8.290000 | 545779855.200000 | 93.089287 | 1.416243 | 1 | |
| EQIX | Equinix | Real Estate | REITs | 302.399994 | 10.019650 | 1.308082 | 7.000000 | 164.000000 | 1617921000.000000 | 187774000.000000 | 3.250000 | 57776615.380000 | 93.046152 | 23.856728 | 1 | |
| EQR | Equity Residential | Real Estate | REITs | 81.589996 | 8.037605 | 1.056186 | 8.000000 | 47.000000 | 2196000.000000 | 870120000.000000 | 2.370000 | 367139240.500000 | 34.426159 | -1.269332 | 1 | |
| EQT | EQT Corporation | Energy | Oil & Gas Exploration & Production | 52.130001 | -21.253771 | 2.364883 | 2.000000 | 201.000000 | 523803000.000000 | 85171000.000000 | 0.560000 | 152091071.400000 | 93.089287 | 9.567952 | 1 | |
| ES | Eversource Energy | Utilities | MultiUtilities | 51.070000 | 0.709921 | 1.232829 | 8.000000 | 1.000000 | -14756000.000000 | 878485000.000000 | 2.770000 | 317142599.300000 | 18.436823 | -1.169833 | 1 | |
| ESS | Essex Property Trust, Inc. | Real Estate | Residential REITs | 239.410004 | 6.765073 | 1.118425 | 4.000000 | 47.000000 | 4073000.000000 | 232120000.000000 | 3.500000 | 66320000.000000 | 68.402858 | -3.089477 | 1 | |
| ETFC | E*Trade | Financials | Investment Banking & Brokerage | 29.639999 | 12.656785 | 1.452048 | 5.000000 | 99.000000 | 450000000.000000 | 268000000.000000 | 0.920000 | 291304347.800000 | 32.217390 | -0.597313 | 1 | |
| ETN | Eaton Corporation | Industrials | Industrial Conglomerates | 52.040001 | 1.166411 | 1.521430 | 13.000000 | 10.000000 | -513000000.000000 | 1979000000.000000 | 4.250000 | 465647058.800000 | 12.244706 | -8.639591 | 1 | |
| ETR | Entergy Corp. | Utilities | Electric Utilities | 68.360001 | 4.910983 | 1.217401 | 2.000000 | 44.000000 | -71065000.000000 | -156734000.000000 | -0.990000 | 158317171.700000 | 18.456543 | 6.174024 | 1 | |
| EW | Edwards Lifesciences | Health Care | Health Care Equipment | 78.980003 | 11.616734 | 1.666482 | 20.000000 | 257.000000 | 64600000.000000 | 494900000.000000 | 2.300000 | 215173913.000000 | 34.339132 | 6.349747 | 1 | |
| EXC | Exelon Corp. | Utilities | MultiUtilities | 27.770000 | -6.403775 | 1.351595 | 9.000000 | 74.000000 | 4624000000.000000 | 2269000000.000000 | 2.685000 | 299887089.100000 | 17.313076 | -1.715880 | 1 | |
| EXPD | Expeditors Int'l | Industrials | Air Freight & Logistics | 45.099998 | -4.449159 | 1.062553 | 27.000000 | 94.000000 | -119311000.000000 | 457223000.000000 | 2.420000 | 188935124.000000 | 18.636363 | 5.991459 | 1 | |
| EXPE | Expedia Inc. | Consumer Discretionary | Internet & Direct Marketing Retail | 124.300003 | 4.894517 | 1.578747 | 16.000000 | 29.000000 | 273599000.000000 | 764465000.000000 | 5.870000 | 130232538.300000 | 21.175469 | -44.103494 | 1 | |
| EXR | Extra Space Storage | Real Estate | Specialized REITs | 88.209999 | 13.922251 | 1.186059 | 19.000000 | 39.000000 | 28136000.000000 | 394950000.000000 | 1.580000 | 249968354.400000 | 55.829113 | -14.151445 | 1 | |
| F | Ford Motor | Consumer Discretionary | Automobile Manufacturers | 14.090000 | 2.398256 | 1.151454 | 26.000000 | 43.000000 | 3515000000.000000 | 7373000000.000000 | 1.860000 | 3963978495.000000 | 7.575269 | 5.108756 | 1 | |
| FAST | Fastenal Co | Industrials | Building Products | 40.820000 | 10.984234 | 1.411396 | 29.000000 | 35.000000 | 14523000.000000 | 516361000.000000 | 1.770000 | 291729378.500000 | 23.062147 | 4.427425 | 1 | |
| FB | Information Technology | Internet Software & Services | 104.660004 | 16.224320 | 1.320606 | 8.000000 | 958.000000 | 592000000.000000 | 3669000000.000000 | 1.310000 | 2800763359.000000 | 79.893133 | 5.884467 | 1 | ||
| FBHS | Fortune Brands Home & Security | Industrials | Building Products | 55.500000 | 16.817517 | 1.348297 | 13.000000 | 31.000000 | 46600000.000000 | 315000000.000000 | 1.970000 | 159898477.200000 | 28.172589 | -2.100708 | 1 | |
| FCX | Freeport-McMoran Cp & Gld | Materials | Copper | 6.770000 | -31.685167 | 3.796410 | 155.000000 | 5.000000 | -240000000.000000 | -12156000000.000000 | -11.310000 | 1074801061.000000 | 22.811951 | 2.935427 | 1 | |
| FE | FirstEnergy Corp | Utilities | Electric Utilities | 31.730000 | 1.179844 | 1.238785 | 5.000000 | 2.000000 | 46000000.000000 | 578000000.000000 | 1.370000 | 421897810.200000 | 23.160584 | -6.072561 | 1 | |
| FIS | Fidelity National Information Services | Information Technology | Internet Software & Services | 60.599998 | -10.553508 | 1.148295 | 7.000000 | 29.000000 | 194800000.000000 | 650800000.000000 | 2.220000 | 293153153.200000 | 27.297296 | -19.086610 | 1 | |
| FISV | Fiserv Inc | Information Technology | Internet Software & Services | 91.459999 | 5.235295 | 0.904487 | 27.000000 | 18.000000 | -19000000.000000 | 712000000.000000 | 3.040000 | 234210526.300000 | 30.085526 | -7.975730 | 1 | |
| FLIR | FLIR Systems | Information Technology | Electronic Equipment & Instruments | 28.070000 | 0.214209 | 1.761193 | 15.000000 | 81.000000 | -58589000.000000 | 241686000.000000 | 1.730000 | 139702890.200000 | 16.225434 | 4.014713 | 1 | |
| FLR | Fluor Corp. | Industrials | Diversified Commercial Services | 47.220001 | 10.819056 | 1.774454 | 14.000000 | 73.000000 | -43239000.000000 | 412512000.000000 | 2.850000 | 144741052.600000 | 16.568421 | 14.992623 | 1 | |
| FLS | Flowserve Corporation | Industrials | Industrial Machinery | 42.080002 | 2.210357 | 1.781661 | 16.000000 | 27.000000 | -83906000.000000 | 267669000.000000 | 2.010000 | 133168656.700000 | 20.935324 | 6.746760 | 1 | |
| FMC | FMC Corporation | Materials | Diversified Chemicals | 39.130001 | 15.088238 | 2.175738 | 26.000000 | 5.000000 | -30900000.000000 | 489000000.000000 | 3.660000 | 133606557.400000 | 10.691257 | 5.101546 | 1 | |
| FRT | Federal Realty Investment Trust | Real Estate | Retail REITs | 146.100006 | 6.806063 | 1.239858 | 13.000000 | 47.000000 | -26905000.000000 | 210219000.000000 | 3.040000 | 69150986.840000 | 48.059213 | -3.973395 | 1 | |
| FSLR | First Solar Inc | Information Technology | Semiconductors | 65.989998 | 55.051683 | 2.075216 | 10.000000 | 190.000000 | -355228000.000000 | 546421000.000000 | 5.420000 | 100815682.700000 | 12.175276 | 22.563791 | 1 | |
| FTR | Frontier Communications | Telecommunications Services | Integrated Telecommunications Services | 4.670000 | -2.301255 | 2.026818 | 3.000000 | 496.000000 | 254000000.000000 | -196000000.000000 | -0.290000 | 675862069.000000 | 14.518987 | 10.497704 | 1 | |
| GD | General Dynamics | Industrials | Aerospace & Defense | 137.360001 | -0.463767 | 0.939544 | 28.000000 | 22.000000 | -1603000000.000000 | 2965000000.000000 | 9.230000 | 321235102.900000 | 14.881907 | 4.242998 | 1 | |
| GGP | General Growth Properties Inc. | Real Estate | Retail REITs | 27.209999 | 4.212937 | 1.390342 | 17.000000 | 47.000000 | -15576000.000000 | 1374561000.000000 | 3.040000 | 158299350.600000 | 47.366973 | -3.973395 | 1 | |
| GILD | Gilead Sciences | Health Care | Biotechnology | 101.190002 | 2.689264 | 1.494060 | 98.000000 | 148.000000 | 2824000000.000000 | 18108000000.000000 | 12.370000 | 1463864188.000000 | 8.180275 | 3.159446 | 1 | |
| GLW | Corning Inc. | Information Technology | Electronic Components | 18.280001 | 6.588927 | 1.578483 | 7.000000 | 163.000000 | -809000000.000000 | 1339000000.000000 | 1.020000 | 1312745098.000000 | 17.921570 | 3.617610 | 1 | |
| GM | General Motors | Consumer Discretionary | Automobile Manufacturers | 34.009998 | 12.281271 | 1.344514 | 24.000000 | 33.000000 | -3857000000.000000 | 9687000000.000000 | 6.110000 | 1585433715.000000 | 5.566284 | -4.892037 | 1 | |
| GPC | Genuine Parts | Consumer Discretionary | Specialty Stores | 85.889999 | 4.033432 | 1.177027 | 22.000000 | 5.000000 | 73901000.000000 | 705672000.000000 | 4.650000 | 151757419.400000 | 18.470968 | 7.205242 | 1 | |
| GRMN | Garmin Ltd. | Consumer Discretionary | Consumer Electronics | 37.169998 | 3.393594 | 1.665475 | 14.000000 | 121.000000 | -363198000.000000 | 456227000.000000 | 2.390000 | 190889958.200000 | 15.552300 | 5.760057 | 1 | |
| GT | Goodyear Tire & Rubber | Consumer Discretionary | Tires & Rubber | 32.669998 | 10.446241 | 1.522778 | 8.000000 | 30.000000 | -685000000.000000 | 307000000.000000 | 1.140000 | 269298245.600000 | 28.657893 | 3.835896 | 1 | |
| GWW | Grainger (W.W.) Inc. | Industrials | Industrial Materials | 202.589996 | -5.336199 | 1.348597 | 34.000000 | 16.000000 | 63492000.000000 | 768996000.000000 | 11.690000 | 65782378.100000 | 17.330196 | 12.112879 | 1 | |
| HAL | Halliburton Co. | Energy | Oil & Gas Equipment & Services | 34.040001 | -5.101751 | 1.966062 | 4.000000 | 189.000000 | 7786000000.000000 | -671000000.000000 | -0.790000 | 849367088.600000 | 93.089287 | 17.345857 | 1 | |
| HAS | Hasbro Inc. | Consumer Discretionary | Leisure Products | 67.360001 | -7.076834 | 1.583355 | 27.000000 | 92.000000 | 83583000.000000 | 451838000.000000 | 3.610000 | 125162880.900000 | 18.659280 | 12.145326 | 1 | |
| HBAN | Huntington Bancshares | Financials | Banks | 11.060000 | 4.143126 | 1.337793 | 11.000000 | 99.000000 | -373409000.000000 | 692957000.000000 | 0.820000 | 845069512.200000 | 13.487805 | -0.065057 | 1 | |
| HCA | HCA Holdings | Health Care | Health Care Facilities | 67.629997 | -12.532337 | 1.914907 | 28.000000 | 13.000000 | 175000000.000000 | 2129000000.000000 | 5.140000 | 414202334.600000 | 13.157587 | -7.279051 | 1 | |
| HCN | Welltower Inc. | Real Estate | REITs | 68.029999 | 0.044116 | 1.341731 | 6.000000 | 47.000000 | -112818000.000000 | 849073000.000000 | 2.350000 | 361307659.600000 | 28.948936 | -3.738047 | 1 | |
| HCP | HCP Inc. | Real Estate | REITs | 34.826959 | 2.218658 | 1.282286 | 6.000000 | 47.000000 | 162690000.000000 | -559235000.000000 | -1.210000 | 462177686.000000 | 33.651715 | -1.269332 | 1 | |
| HES | Hess Corporation | Energy | Integrated Oil & Gas | 48.480000 | -4.585713 | 2.398580 | 16.000000 | 103.000000 | 272000000.000000 | -3056000000.000000 | -10.780000 | 283487940.600000 | 28.407929 | 6.264817 | 1 | |
| HIG | Hartford Financial Svc.Gp. | Financials | Property & Casualty Insurance | 43.459999 | -5.005467 | 1.147332 | 10.000000 | 99.000000 | 49000000.000000 | 1682000000.000000 | 4.050000 | 415308642.000000 | 10.730864 | -4.327138 | 1 | |
| HOG | Harley-Davidson | Consumer Discretionary | Motorcycle Manufacturers | 45.389999 | -17.247036 | 1.560372 | 41.000000 | 31.000000 | -184471000.000000 | 752207000.000000 | 3.710000 | 202751212.900000 | 12.234501 | 6.069389 | 1 | |
| HON | Honeywell Int'l Inc. | Industrials | Industrial Conglomerates | 103.570000 | 9.320247 | 1.103449 | 26.000000 | 41.000000 | -1504000000.000000 | 4768000000.000000 | 6.110000 | 780360065.500000 | 16.950900 | -3.709826 | 1 | |
| HPE | Hewlett Packard Enterprise | Information Technology | Technology Hardware, Storage & Peripherals | 15.200000 | -17.837838 | 3.400491 | 7.000000 | 45.000000 | 7523000000.000000 | 2461000000.000000 | 1.800000 | 513987730.100000 | 25.309524 | 3.954975 | 1 | |
| HPQ | HP Inc. | Information Technology | Computer Hardware | 11.840000 | 2.161759 | 2.373359 | 16.000000 | 18.000000 | 2300000000.000000 | 4554000000.000000 | 1.800000 | 513987730.100000 | 25.309524 | 3.954975 | 1 | |
| HRL | Hormel Foods Corp. | Consumer Staples | Packaged Foods & Meats | 39.540001 | 24.496225 | 1.078455 | 17.000000 | 29.000000 | 13065000.000000 | 686088000.000000 | 3.280000 | 199237804.900000 | 24.070121 | -1.980483 | 1 | |
| HSIC | Henry Schein | Health Care | Health Care Distributors | 158.190002 | 18.317132 | 1.013922 | 17.000000 | 3.000000 | -17388000.000000 | 479058000.000000 | 5.780000 | 82882006.920000 | 27.368512 | 5.925677 | 1 | |
| HST | Host Hotels & Resorts | Real Estate | REITs | 15.340000 | -3.217666 | 1.594628 | 8.000000 | 47.000000 | -445000000.000000 | 558000000.000000 | 0.220000 | 2536363636.000000 | 69.727273 | -0.113548 | 1 | |
| HSY | The Hershey Company | Consumer Staples | Packaged Foods & Meats | 89.269997 | -3.261814 | 1.188383 | 51.000000 | 16.000000 | -28325000.000000 | 512951000.000000 | 3.280000 | 199237804.900000 | 24.070121 | -1.980483 | 1 | |
| HUM | Humana Inc. | Health Care | Managed Health Care | 178.509995 | -0.145443 | 1.615206 | 12.000000 | 70.000000 | 636000000.000000 | 1276000000.000000 | 8.540000 | 149414519.900000 | 20.902810 | -8.805281 | 1 | |
| IBM | International Business Machines | Information Technology | IT Consulting & Other Services | 137.619995 | -5.292136 | 1.082881 | 92.000000 | 24.000000 | -790000000.000000 | 13190000000.000000 | 13.480000 | 978486646.900000 | 10.209198 | 4.852391 | 1 | |
| IDXX | IDEXX Laboratories | Health Care | Health Care Equipment | 72.919998 | -1.565880 | 1.469586 | 228.000000 | 40.000000 | -193542000.000000 | 192078000.000000 | 2.070000 | 92791304.350000 | 35.227052 | -0.981083 | 1 | |
| IFF | Intl Flavors & Fragrances | Materials | Specialty Chemicals | 119.639999 | 14.961083 | 1.152855 | 26.000000 | 25.000000 | -296585000.000000 | 419247000.000000 | 5.190000 | 80779768.790000 | 23.052023 | 5.047700 | 1 | |
| INTC | Intel Corp. | Information Technology | Semiconductors | 34.450001 | 14.035095 | 1.226022 | 19.000000 | 162.000000 | 12747000000.000000 | 11420000000.000000 | 2.410000 | 4738589212.000000 | 14.294606 | 3.954975 | 1 | |
| IP | International Paper | Materials | Paper Packaging | 37.700001 | -0.026513 | 1.301630 | 24.000000 | 27.000000 | -831000000.000000 | 938000000.000000 | 2.250000 | 416888888.900000 | 16.755556 | 6.123934 | 1 | |
| IPG | Interpublic Group | Consumer Discretionary | Advertising | 23.280001 | 21.821035 | 1.139799 | 23.000000 | 20.000000 | -157700000.000000 | 454600000.000000 | 1.110000 | 409549549.600000 | 20.972974 | 0.265658 | 1 | |
| IRM | Iron Mountain Incorporated | Real Estate | REITs | 27.010000 | -13.067267 | 1.301382 | 24.000000 | 15.000000 | 2448000.000000 | 123241000.000000 | 0.580000 | 212484482.800000 | 46.568966 | -2.763651 | 1 | |
| ISRG | Intuitive Surgical Inc. | Health Care | Health Care Equipment | 546.159973 | 18.733013 | 1.126009 | 14.000000 | 317.000000 | 114300000.000000 | 588800000.000000 | 15.870000 | 37101449.280000 | 34.414617 | 42.607500 | 1 | |
| ITW | Illinois Tool Works | Industrials | Industrial Machinery | 92.680000 | 12.776831 | 1.142869 | 36.000000 | 130.000000 | -900000000.000000 | 1899000000.000000 | 5.160000 | 368023255.800000 | 17.961240 | 7.586477 | 1 | |
| IVZ | Invesco Ltd. | Financials | Asset Management & Custody Banks | 33.480000 | 7.067477 | 1.580839 | 12.000000 | 67.000000 | 412000000.000000 | 968100000.000000 | 2.260000 | 428362831.900000 | 14.814159 | 4.218620 | 1 | |
| JBHT | J. B. Hunt Transport Services | Industrials | Trucking | 73.360001 | 2.961405 | 1.218373 | 33.000000 | 1.000000 | -395000.000000 | 427235000.000000 | 3.690000 | 115781842.800000 | 19.880759 | 2.823845 | 1 | |
| JEC | Jacobs Engineering Group | Industrials | Industrial Conglomerates | 41.950001 | 11.539484 | 1.732990 | 7.000000 | 23.000000 | -271788000.000000 | 302971000.000000 | 2.420000 | 125194628.100000 | 17.334711 | 6.294943 | 1 | |
| JNPR | Juniper Networks | Information Technology | Networking Equipment | 27.600000 | 7.351229 | 1.841767 | 14.000000 | 108.000000 | -218700000.000000 | 633700000.000000 | 1.620000 | 391172839.500000 | 17.037037 | 2.752236 | 1 | |
| JPM | JPMorgan Chase & Co. | Financials | Banks | 66.029999 | 8.033377 | 1.130337 | 10.000000 | 99.000000 | -7341000000.000000 | 24442000000.000000 | 6.050000 | 4040000000.000000 | 10.914049 | -1.886881 | 1 | |
| KIM | Kimco Realty | Real Estate | REITs | 26.459999 | 8.709938 | 1.224688 | 18.000000 | 47.000000 | 2212000.000000 | 894115000.000000 | 2.010000 | 444833333.300000 | 13.164179 | -1.269332 | 1 | |
| KMB | Kimberly-Clark | Consumer Staples | Household Products | 127.300003 | 17.511309 | 0.870405 | 582.000000 | 10.000000 | -170000000.000000 | 1013000000.000000 | 2.780000 | 364388489.200000 | 45.791368 | -2.533011 | 1 | |
| KMI | Kinder Morgan | Energy | Oil & Gas Refining & Marketing & Transportation | 14.920000 | -47.129693 | 3.139352 | 1.000000 | 7.000000 | -86000000.000000 | 253000000.000000 | 0.100000 | 2530000000.000000 | 149.200000 | -1.894071 | 1 | |
| KO | Coca Cola Company | Consumer Staples | Soft Drinks | 42.959999 | 6.812526 | 0.889913 | 29.000000 | 74.000000 | -1649000000.000000 | 7351000000.000000 | 1.690000 | 4349704142.000000 | 25.420118 | -1.466307 | 1 | |
| KSU | Kansas City Southern | Industrials | Railroads | 74.669998 | -18.438017 | 2.071639 | 12.000000 | 18.000000 | -211400000.000000 | 483500000.000000 | 4.410000 | 109637188.200000 | 16.931972 | -2.012091 | 1 | |
| LEG | Leggett & Platt | Industrials | Industrial Conglomerates | 42.020000 | 1.965545 | 1.204037 | 30.000000 | 36.000000 | -79600000.000000 | 329200000.000000 | 2.310000 | 142510822.500000 | 18.190476 | 2.931005 | 1 | |
| LEN | Lennar Corp. | Consumer Discretionary | Homebuilding | 48.910000 | 1.705136 | 1.569167 | 14.000000 | 25.000000 | -123369000.000000 | 802894000.000000 | 3.870000 | 207466149.900000 | 12.638243 | -0.307832 | 1 | |
| LH | Laboratory Corp. of America Holding | Health Care | Health Care Facilities | 123.639999 | 14.174899 | 1.603130 | 9.000000 | 42.000000 | 136400000.000000 | 436900000.000000 | 5.030000 | 178246546.300000 | 15.900937 | -1.294844 | 1 | |
| LKQ | LKQ Corporation | Consumer Discretionary | Distributors | 29.629999 | 4.441304 | 1.427237 | 14.000000 | 12.000000 | -27208000.000000 | 423223000.000000 | 3.030000 | 213598256.500000 | 20.819876 | -0.857290 | 1 | |
| LLL | L-3 Communications Holdings | Industrials | Industrial Conglomerates | 119.510002 | 14.539013 | 1.513434 | 6.000000 | 7.000000 | -235000000.000000 | -240000000.000000 | -2.970000 | 80808080.810000 | 17.334711 | 14.280750 | 1 | |
| LLY | Lilly (Eli) & Co. | Health Care | Pharmaceuticals | 84.260002 | 0.789478 | 1.440622 | 17.000000 | 54.000000 | -205200000.000000 | 2408400000.000000 | 2.270000 | 1060969163.000000 | 37.118944 | -0.651103 | 1 | |
| LMT | Lockheed Martin Corp. | Industrials | Aerospace & Defense | 217.149994 | 5.254227 | 0.903098 | 116.000000 | 8.000000 | -356000000.000000 | 3605000000.000000 | 11.620000 | 310240963.900000 | 18.687607 | -10.852854 | 1 | |
| LNT | Alliant Energy Corp | Utilities | Electric Utilities | 31.225001 | 6.642759 | 1.115842 | 10.000000 | 0.000000 | -51100000.000000 | 388400000.000000 | 3.360000 | 115595238.100000 | 9.293155 | -4.606591 | 1 | |
| LUK | Leucadia National Corp. | Financials | Multi-Sector Holdings | 17.389999 | -14.292764 | 1.554235 | 2.000000 | 81.000000 | -638127000.000000 | 252111000.000000 | 0.740000 | 340690540.500000 | 23.499999 | 19.821416 | 1 | |
| LUV | Southwest Airlines | Industrials | Airlines | 43.060001 | 13.855106 | 1.536290 | 30.000000 | 41.000000 | 301000000.000000 | 2181000000.000000 | 3.300000 | 660909090.900000 | 13.048485 | -5.117194 | 1 | |
| LVLT | Level 3 Communications | Telecommunications Services | Alternative Carriers | 54.360001 | 24.707504 | 1.457013 | 34.000000 | 61.000000 | 274000000.000000 | 3433000000.000000 | 9.710000 | 353553038.100000 | 5.598352 | -2.245773 | 1 | |
| LYB | LyondellBasell | Materials | Diversified Chemicals | 86.900002 | 2.573183 | 1.609745 | 68.000000 | 46.000000 | -107000000.000000 | 4476000000.000000 | 9.620000 | 465280665.300000 | 9.033264 | 10.316354 | 1 | |
| MA | Mastercard Inc. | Information Technology | Internet Software & Services | 97.360001 | 7.496965 | 1.095876 | 63.000000 | 130.000000 | 610000000.000000 | 3808000000.000000 | 3.360000 | 1133333333.000000 | 28.976191 | 3.451765 | 1 | |
| MAA | Mid-America Apartments | Real Estate | Residential REITs | 90.809998 | 10.622490 | 1.177776 | 12.000000 | 47.000000 | 10906000.000000 | 350745000.000000 | 4.410000 | 79534013.610000 | 20.591836 | -3.089477 | 1 | |
| MAC | Macerich | Real Estate | Retail REITs | 80.690002 | 4.183351 | 1.169328 | 10.000000 | 47.000000 | 1603000.000000 | 487562000.000000 | 3.080000 | 158299350.600000 | 26.198053 | -3.973395 | 1 | |
| MAR | Marriott Int'l. | Consumer Discretionary | Hotels, Resorts & Cruise Lines | 67.040001 | -1.973970 | 1.642450 | 24.000000 | 3.000000 | -8000000.000000 | 859000000.000000 | 3.220000 | 266770186.300000 | 20.819876 | -12.370198 | 1 | |
| MAS | Masco Corp. | Industrials | Building Products | 28.299999 | 11.637077 | 1.428359 | 263.000000 | 61.000000 | 85000000.000000 | 355000000.000000 | 1.030000 | 344660194.200000 | 27.475727 | 2.219577 | 1 | |
| MAT | Mattel Inc. | Consumer Discretionary | Leisure Products | 27.170000 | 30.625006 | 1.921708 | 14.000000 | 54.000000 | -78836000.000000 | 369416000.000000 | 1.080000 | 342051851.900000 | 25.157407 | 4.535251 | 1 | |
| MCD | McDonald's Corp. | Consumer Discretionary | Restaurants | 118.139999 | 19.939085 | 0.733163 | 64.000000 | 260.000000 | 5607600000.000000 | 4529300000.000000 | 4.820000 | 939688796.700000 | 24.510373 | 7.122145 | 1 | |
| MCO | Moody's Corp | Financials | Diversified Financial Services | 100.339996 | 2.345976 | 1.268800 | 167.000000 | 183.000000 | 537900000.000000 | 941300000.000000 | 4.700000 | 200276595.700000 | 21.348935 | 8.615585 | 1 | |
| MDLZ | Mondelez International | Consumer Staples | Packaged Foods & Meats | 44.840000 | 6.079962 | 1.321548 | 26.000000 | 17.000000 | 239000000.000000 | 7267000000.000000 | 4.490000 | 1618485523.000000 | 9.986637 | -12.809506 | 1 | |
| MET | MetLife Inc. | Financials | Life & Health Insurance | 48.209999 | 1.366690 | 1.138650 | 8.000000 | 99.000000 | 1944000000.000000 | 5310000000.000000 | 4.610000 | 1151843818.000000 | 10.457700 | -1.883912 | 1 | |
| MHK | Mohawk Industries | Consumer Discretionary | Home Furnishings | 189.389999 | 3.514425 | 1.492478 | 13.000000 | 3.000000 | -16185000.000000 | 615302000.000000 | 2.590000 | 237568339.800000 | 73.123552 | -3.980316 | 1 | |
| MJN | Mead Johnson | Consumer Staples | Packaged Foods & Meats | 78.949997 | 12.081196 | 1.718403 | 103.000000 | 136.000000 | 403700000.000000 | 653500000.000000 | 3.280000 | 199237804.900000 | 24.070121 | 6.495755 | 1 | |
| MKC | McCormick & Co. | Consumer Staples | Packaged Foods & Meats | 85.559998 | 6.976738 | 1.032221 | 24.000000 | 9.000000 | 35300000.000000 | 401600000.000000 | 3.140000 | 127898089.200000 | 27.248407 | -1.980483 | 1 | |
| MLM | Martin Marietta Materials | Materials | Construction Materials | 136.580002 | -10.866015 | 2.164150 | 7.000000 | 46.000000 | 59758000.000000 | 288792000.000000 | 4.310000 | 67005104.410000 | 31.689096 | 3.050887 | 1 | |
| MMC | Marsh & McLennan | Financials | Insurance Brokers | 55.450001 | 6.022948 | 1.034162 | 25.000000 | 99.000000 | -584000000.000000 | 1599000000.000000 | 3.010000 | 531229235.900000 | 18.421927 | -1.950194 | 1 | |
| MMM | 3M Company | Industrials | Industrial Conglomerates | 150.639999 | 5.927847 | 0.982698 | 42.000000 | 27.000000 | -99000000.000000 | 4833000000.000000 | 7.720000 | 626036269.400000 | 19.512953 | 2.023844 | 1 | |
| MNST | Monster Beverage | Consumer Staples | Soft Drinks | 49.653332 | 10.800357 | 1.585944 | 11.000000 | 568.000000 | 1805094000.000000 | 546733000.000000 | 3.710000 | 1469541779.000000 | 25.420118 | -5.190734 | 1 | |
| MO | Altria Group Inc | Consumer Staples | Tobacco | 58.209999 | 6.885788 | 0.959008 | 182.000000 | 33.000000 | -952000000.000000 | 5241000000.000000 | 2.670000 | 1962921348.000000 | 21.801498 | -6.632971 | 1 | |
| MOS | The Mosaic Company | Materials | Fertilizers & Agricultural Chemicals | 27.590000 | -11.229086 | 2.830675 | 10.000000 | 62.000000 | -1098300000.000000 | 1000400000.000000 | 2.790000 | 358566308.200000 | 9.888889 | 5.846617 | 1 | |
| MPC | Marathon Petroleum | Energy | Oil & Gas Refining & Marketing & Transportation | 51.840000 | 11.507846 | 1.989371 | 22.000000 | 18.000000 | -367000000.000000 | 2852000000.000000 | 5.290000 | 539130434.800000 | 9.799622 | 5.798226 | 1 | |
| MRK | Merck & Co. | Health Care | Pharmaceuticals | 52.820000 | 7.031413 | 1.278460 | 10.000000 | 70.000000 | 1083000000.000000 | 4442000000.000000 | 1.580000 | 2811392405.000000 | 33.430380 | -4.282931 | 1 | |
| MRO | Marathon Oil Corp. | Energy | Oil & Gas Exploration & Production | 12.590000 | -20.265991 | 3.325386 | 12.000000 | 71.000000 | -1177000000.000000 | -2204000000.000000 | -3.260000 | 676073619.600000 | 93.089287 | 1.273530 | 1 | |
| MTB | M&T Bank Corp. | Financials | Banks | 121.180000 | -0.361785 | 1.380390 | 7.000000 | 99.000000 | -5317000.000000 | 1079667000.000000 | 7.220000 | 149538365.700000 | 16.783934 | -0.938007 | 1 | |
| MTD | Mettler Toledo | Health Care | Life Sciences Tools & Services | 339.130005 | 18.942905 | 1.115376 | 61.000000 | 18.000000 | 13624000.000000 | 352820000.000000 | 12.750000 | 27672156.860000 | 26.598432 | 4.403994 | 1 | |
| MUR | Murphy Oil | Energy | Integrated Oil & Gas | 22.450001 | -8.591197 | 2.851180 | 43.000000 | 27.000000 | -910125000.000000 | -2270833000.000000 | -13.030000 | 174277283.200000 | 28.407929 | -1.298006 | 1 | |
| MYL | Mylan N.V. | Health Care | Pharmaceuticals | 54.070000 | 33.177346 | 2.299304 | 9.000000 | 30.000000 | 1010500000.000000 | 847600000.000000 | 1.580000 | 1633015873.000000 | 33.430380 | -4.213309 | 1 | |
| NAVI | Navient | Financials | Consumer Finance | 11.450000 | 1.868327 | 2.230827 | 25.000000 | 99.000000 | 151000000.000000 | 997000000.000000 | 2.660000 | 374812030.100000 | 4.304511 | -1.880943 | 1 | |
| NBL | Noble Energy Inc | Energy | Oil & Gas Exploration & Production | 32.930000 | 7.298791 | 2.509437 | 24.000000 | 57.000000 | -155000000.000000 | -2441000000.000000 | -6.070000 | 402141680.400000 | 93.089287 | 1.171229 | 1 | |
| NDAQ | NASDAQ OMX Group | Financials | Diversified Financial Services | 58.169998 | 8.810324 | 1.563258 | 8.000000 | 117.000000 | -126000000.000000 | 428000000.000000 | 2.560000 | 167187500.000000 | 22.722655 | -11.717383 | 1 | |
| NEE | NextEra Energy | Utilities | MultiUtilities | 103.889999 | 6.237855 | 1.023375 | 12.000000 | 6.000000 | -6000000.000000 | 2752000000.000000 | 6.110000 | 450409165.300000 | 17.003273 | -7.353314 | 1 | |
| NEM | Newmont Mining Corp. (Hldg. Co.) | Materials | Gold | 17.990000 | 10.844116 | 2.536050 | 2.000000 | 198.000000 | 379000000.000000 | 220000000.000000 | 0.430000 | 511627907.000000 | 41.837209 | 6.971864 | 1 | |
| NFLX | Netflix Inc. | Information Technology | Internet Software & Services | 114.379997 | 11.145654 | 2.605949 | 6.000000 | 65.000000 | 695722000.000000 | 122641000.000000 | 0.290000 | 422900000.000000 | 394.413783 | -5.700168 | 1 | |
| NFX | Newfield Exploration Co | Energy | Oil & Gas Exploration & Production | 32.560001 | -3.296695 | 2.421529 | 244.000000 | 1.000000 | -9000000.000000 | -3362000000.000000 | -21.180000 | 158734655.300000 | 93.089287 | -0.138596 | 1 | |
| NLSN | Nielsen Holdings | Industrials | Research & Consulting Services | 46.599998 | 4.931317 | 1.198493 | 13.000000 | 21.000000 | 84000000.000000 | 570000000.000000 | 1.550000 | 367741935.500000 | 30.064515 | -12.375526 | 1 | |
| NOV | National Oilwell Varco Inc. | Energy | Oil & Gas Equipment & Services | 33.490002 | -12.558739 | 1.952020 | 5.000000 | 49.000000 | -1456000000.000000 | -769000000.000000 | -1.990000 | 386432160.800000 | 93.089287 | 9.582536 | 1 | |
| NSC | Norfolk Southern Corp. | Industrials | Railroads | 84.589996 | 9.529966 | 2.168814 | 13.000000 | 49.000000 | 128000000.000000 | 1556000000.000000 | 5.130000 | 303313840.200000 | 16.489278 | 0.926433 | 1 | |
| NTRS | Northern Trust Corp. | Financials | Asset Management & Custody Banks | 72.089996 | 5.796884 | 1.281566 | 11.000000 | 67.000000 | 3394000000.000000 | 973800000.000000 | 4.030000 | 241637717.100000 | 17.888336 | -13.398380 | 1 | |
| NUE | Nucor Corp. | Materials | Steel | 40.299999 | 6.585554 | 1.460619 | 5.000000 | 147.000000 | 915325000.000000 | 357659000.000000 | 1.110000 | 322215315.300000 | 36.306305 | 11.168107 | 1 | |
| NWL | Newell Brands | Consumer Discretionary | Housewares & Specialties | 44.080002 | 9.980039 | 1.641300 | 19.000000 | 14.000000 | 75400000.000000 | 350000000.000000 | 1.300000 | 269230769.200000 | 33.907694 | -2.075543 | 1 | |
| O | Realty Income Corporation | Real Estate | Retail REITs | 51.630001 | 8.420836 | 1.104581 | 4.000000 | 47.000000 | 36442000.000000 | 283766000.000000 | 1.090000 | 260335779.800000 | 47.366973 | -3.973395 | 1 | |
| OKE | ONEOK | Energy | Oil & Gas Exploration & Production | 24.660000 | -24.123077 | 3.560178 | 73.000000 | 6.000000 | -75150000.000000 | 244977000.000000 | 1.170000 | 209382051.300000 | 21.076923 | -8.025110 | 1 | |
| OMC | Omnicom Group | Consumer Discretionary | Advertising | 75.660004 | 14.810321 | 1.066369 | 45.000000 | 18.000000 | 217100000.000000 | 1093900000.000000 | 4.430000 | 246930022.600000 | 17.079008 | -10.464098 | 1 | |
| ORLY | O'Reilly Automotive | Consumer Discretionary | Specialty Stores | 253.419998 | 0.964143 | 1.089370 | 47.000000 | 4.000000 | -134259000.000000 | 931216000.000000 | 9.320000 | 99915879.830000 | 27.190987 | -0.364026 | 1 | |
| OXY | Occidental Petroleum | Energy | Oil & Gas Exploration & Production | 67.610001 | 0.865287 | 1.589520 | 32.000000 | 64.000000 | -588000000.000000 | -7829000000.000000 | -10.230000 | 765298142.700000 | 93.089287 | 3.345102 | 1 | |
| PBCT | People's United Financial | Financials | Thrifts & Mortgage Finance | 16.150000 | 3.128991 | 1.132813 | 5.000000 | 99.000000 | -298400000.000000 | 260100000.000000 | 0.860000 | 302441860.500000 | 18.779070 | -0.426859 | 1 | |
| PBI | Pitney-Bowes | Industrials | Technology, Hardware, Software and Supplies | 20.650000 | 3.821021 | 1.259611 | 228.000000 | 34.000000 | -403561000.000000 | 407943000.000000 | 2.040000 | 199972058.800000 | 10.122549 | -0.733207 | 1 | |
| PCAR | PACCAR Inc. | Industrials | Construction & Farm Machinery & Heavy Trucks | 47.400002 | -9.317004 | 1.439564 | 23.000000 | 110.000000 | 278800000.000000 | 1604000000.000000 | 4.520000 | 354867256.600000 | 10.486726 | 6.290521 | 1 | |
| PCG | PG&E Corp. | Utilities | MultiUtilities | 53.189999 | 0.510206 | 1.039803 | 5.000000 | 6.000000 | -28000000.000000 | 888000000.000000 | 1.810000 | 490607734.800000 | 29.386740 | -1.121059 | 1 | |
| PCLN | Priceline.com Inc | Consumer Discretionary | Internet & Direct Marketing Retail | 1274.949951 | 3.190527 | 1.268340 | 29.000000 | 184.000000 | -1671386000.000000 | 2551360000.000000 | 50.090000 | 50935516.070000 | 25.453183 | -1.052429 | 1 | |
| PEG | Public Serv. Enterprise Inc. | Utilities | Electric Utilities | 38.689999 | -8.230553 | 1.180661 | 13.000000 | 11.000000 | -8000000.000000 | 1679000000.000000 | 3.320000 | 505722891.600000 | 11.653614 | -0.361858 | 1 | |
| PEP | PepsiCo Inc. | Consumer Staples | Soft Drinks | 99.919998 | 6.072188 | 0.805357 | 45.000000 | 68.000000 | 2962000000.000000 | 5452000000.000000 | 3.710000 | 1469541779.000000 | 26.932614 | -5.190734 | 1 | |
| PFE | Pfizer Inc. | Health Care | Pharmaceuticals | 32.279999 | 3.130991 | 1.238748 | 11.000000 | 79.000000 | 298000000.000000 | 6960000000.000000 | 1.130000 | 6159292035.000000 | 28.566371 | -4.213309 | 1 | |
| PFG | Principal Financial Group | Financials | Diversified Financial Services | 44.980000 | -5.305263 | 1.528985 | 13.000000 | 117.000000 | 700900000.000000 | 1234000000.000000 | 4.110000 | 300243309.000000 | 10.944039 | -4.526995 | 1 | |
| PG | Procter & Gamble | Consumer Staples | Personal Products | 79.410004 | 10.660538 | 0.806056 | 17.000000 | 129.000000 | 160383000.000000 | 636056000.000000 | 3.280000 | 491391569.000000 | 24.070121 | -2.256747 | 1 | |
| PGR | Progressive Corp. | Financials | Property & Casualty Insurance | 31.799999 | 3.515625 | 1.086898 | 17.000000 | 99.000000 | 116000000.000000 | 1267600000.000000 | 2.160000 | 586851851.900000 | 14.722222 | -0.843313 | 1 | |
| PHM | Pulte Homes Inc. | Consumer Discretionary | Homebuilding | 17.820000 | -5.564393 | 1.694751 | 10.000000 | 25.000000 | -533785000.000000 | 494090000.000000 | 1.380000 | 358036231.900000 | 12.913043 | -0.307832 | 1 | |
| PM | Philip Morris International | Consumer Staples | Tobacco | 87.910004 | 10.328820 | 0.861453 | 52.000000 | 22.000000 | 1735000000.000000 | 6873000000.000000 | 4.420000 | 1554977376.000000 | 19.889141 | -1.418027 | 1 | |
| PNC | PNC Financial Services | Financials | Banks | 95.309998 | 6.993709 | 1.120534 | 9.000000 | 99.000000 | -295000000.000000 | 4106000000.000000 | 7.520000 | 546010638.300000 | 12.674202 | -0.694126 | 1 | |
| PNR | Pentair Ltd. | Industrials | Industrial Machinery | 49.529999 | -3.034462 | 1.875910 | 2.000000 | 8.000000 | 15900000.000000 | -76400000.000000 | -0.420000 | 181904761.900000 | 14.579221 | -6.574869 | 1 | |
| PNW | Pinnacle West Capital | Utilities | MultiUtilities | 64.480003 | 0.498752 | 1.143421 | 10.000000 | 3.000000 | 31884000.000000 | 437257000.000000 | 3.940000 | 110978934.000000 | 16.365483 | -6.089228 | 1 | |
| PPG | PPG Industries | Materials | Diversified Chemicals | 98.820000 | 19.160738 | 1.533003 | 28.000000 | 31.000000 | 625000000.000000 | 1406000000.000000 | 5.180000 | 271428571.400000 | 19.077220 | -0.593158 | 1 | |
| PPL | PPL Corp. | Utilities | Electric Utilities | 34.130001 | 3.424245 | 1.109059 | 7.000000 | 22.000000 | -563000000.000000 | 682000000.000000 | 1.010000 | 675247524.800000 | 33.792080 | -2.827111 | 1 | |
| PRU | Prudential Financial | Financials | Diversified Financial Services | 81.410004 | 6.585503 | 1.227467 | 13.000000 | 117.000000 | 2694000000.000000 | 5642000000.000000 | 12.370000 | 456103476.200000 | 6.581245 | -4.178927 | 1 | |
| PSX | Phillips 66 | Energy | Oil & Gas Refining & Marketing & Transportation | 81.800003 | 5.371643 | 1.379589 | 18.000000 | 41.000000 | -2133000000.000000 | 4227000000.000000 | 7.780000 | 543316195.400000 | 10.514139 | 7.029056 | 1 | |
| PWR | Quanta Services Inc. | Industrials | Industrial Conglomerates | 20.250000 | -16.632362 | 2.954291 | 10.000000 | 11.000000 | -61744000.000000 | 321824000.000000 | 1.590000 | 202405031.400000 | 12.735849 | 4.291894 | 1 | |
| PX | Praxair Inc. | Materials | Industrial Gases | 102.400002 | 0.293834 | 1.131240 | 35.000000 | 8.000000 | 21000000.000000 | 1547000000.000000 | 5.390000 | 287012987.000000 | 18.998145 | 0.574887 | 1 | |
| PYPL | PayPal | Information Technology | Data Processing & Outsourced Services | 36.200001 | 17.456201 | 1.925754 | 9.000000 | 25.000000 | -808000000.000000 | 1228000000.000000 | 1.000000 | 1228000000.000000 | 36.200001 | 5.434039 | 1 | |
| R | Ryder System | Industrials | Industrial Conglomerates | 56.830002 | -23.244191 | 1.945966 | 15.000000 | 4.000000 | 10853000.000000 | 304768000.000000 | 5.750000 | 53003130.430000 | 9.883479 | -12.020894 | 1 | |
| RCL | Royal Caribbean Cruises Ltd | Consumer Discretionary | Hotels, Resorts & Cruise Lines | 101.209999 | 13.425973 | 1.556512 | 8.000000 | 9.000000 | -67676000.000000 | 665783000.000000 | 3.030000 | 219730363.000000 | 33.402640 | -15.727481 | 1 | |
| REGN | Regeneron | Health Care | Biotechnology | 542.869995 | 16.995320 | 1.802345 | 17.000000 | 129.000000 | 160383000.000000 | 636056000.000000 | 6.170000 | 103088492.700000 | 87.985412 | 20.409000 | 1 | |
| RHI | Robert Half International | Industrials | Human Resource & Employment Services | 47.139999 | -7.659158 | 1.142370 | 36.000000 | 34.000000 | -62542000.000000 | 357796000.000000 | 2.720000 | 131542647.100000 | 17.330882 | 4.089472 | 1 | |
| ROP | Roper Industries | Industrials | Industrial Conglomerates | 189.789993 | 20.432767 | 1.058807 | 13.000000 | 108.000000 | 168081000.000000 | 696067000.000000 | 6.920000 | 100587716.800000 | 27.426300 | -16.215469 | 1 | |
| RRC | Range Resources Corp. | Energy | Oil & Gas Exploration & Production | 24.610001 | -25.106512 | 3.712995 | 26.000000 | 0.000000 | 23000.000000 | -713685000.000000 | -4.290000 | 166360139.900000 | 93.089287 | 0.525090 | 1 | |
| RSG | Republic Services Inc | Industrials | Industrial Conglomerates | 43.990002 | 6.745943 | 0.839821 | 10.000000 | 2.000000 | -42800000.000000 | 749900000.000000 | 2.140000 | 350420560.700000 | 20.556076 | -2.428225 | 1 | |
| SCG | SCANA Corp | Utilities | MultiUtilities | 60.490002 | 7.232764 | 1.266240 | 14.000000 | 9.000000 | 39000000.000000 | 746000000.000000 | 5.220000 | 142911877.400000 | 11.588123 | -4.016461 | 1 | |
| SCHW | Charles Schwab Corporation | Financials | Investment Banking & Brokerage | 32.930000 | 15.462833 | 1.456940 | 11.000000 | 99.000000 | 615000000.000000 | 1447000000.000000 | 1.040000 | 1391346154.000000 | 31.663462 | -0.130090 | 1 | |
| SE | Spectra Energy Corp. | Energy | Oil & Gas Refining & Marketing & Transportation | 23.940001 | -9.898378 | 2.030786 | 3.000000 | 6.000000 | -2000000.000000 | 196000000.000000 | 0.290000 | 675862069.000000 | 82.551728 | -2.580408 | 1 | |
| SEE | Sealed Air | Materials | Paper Packaging | 44.599998 | -5.146750 | 1.580117 | 43.000000 | 19.000000 | 73000000.000000 | 225400000.000000 | 1.630000 | 138282208.600000 | 27.361962 | -2.716908 | 1 | |
| SHW | Sherwin-Williams | Materials | Specialty Chemicals | 259.600006 | 16.537983 | 1.426488 | 121.000000 | 10.000000 | 165012000.000000 | 1053849000.000000 | 11.380000 | 92605360.280000 | 22.811951 | 2.825366 | 1 | |
| SLG | SL Green Realty | Real Estate | Office REITs | 112.980003 | 4.004424 | 1.091967 | 4.000000 | 47.000000 | -26010000.000000 | 284084000.000000 | 1.020000 | 278513725.500000 | 110.764709 | -3.089477 | 1 | |
| SNI | Scripps Networks Interactive Inc. | Consumer Discretionary | Broadcasting & Cable TV | 55.209999 | 12.238260 | 1.773865 | 40.000000 | 23.000000 | -654720000.000000 | 606828000.000000 | 4.680000 | 129664102.600000 | 11.797008 | -7.961579 | 1 | |
| SO | Southern Co. | Utilities | Electric Utilities | 46.790001 | 4.372070 | 0.895059 | 11.000000 | 15.000000 | 694000000.000000 | 2421000000.000000 | 2.600000 | 931153846.200000 | 17.996154 | -2.795456 | 1 | |
| SPG | Simon Property Group Inc | Real Estate | REITs | 194.440002 | 5.284822 | 1.135546 | 48.000000 | 47.000000 | 88852000.000000 | 2139375000.000000 | 5.880000 | 363839285.700000 | 33.068028 | -1.269332 | 1 | |
| SPGI | S&P Global, Inc. | Financials | Diversified Financial Services | 98.580002 | 14.044424 | 1.080858 | 596.000000 | 51.000000 | -1016000000.000000 | 1156000000.000000 | 4.260000 | 271361502.300000 | 23.140846 | -4.178927 | 1 | |
| SRCL | Stericycle Inc | Industrials | Industrial Conglomerates | 120.599998 | -13.906342 | 1.203816 | 10.000000 | 9.000000 | 33398000.000000 | 267046000.000000 | 3.020000 | 88425827.810000 | 39.933774 | -18.864194 | 1 | |
| SRE | Sempra Energy | Utilities | MultiUtilities | 94.010002 | -2.791849 | 1.126448 | 11.000000 | 9.000000 | -167000000.000000 | 1350000000.000000 | 5.430000 | 248618784.500000 | 17.313076 | -8.547222 | 1 | |
| STI | SunTrust Banks | Financials | Banks | 42.840000 | 11.970732 | 1.437938 | 8.000000 | 99.000000 | -2630000000.000000 | 1933000000.000000 | 3.620000 | 533977900.600000 | 11.834254 | -2.481376 | 1 | |
| STT | State Street Corp. | Financials | Diversified Financial Services | 66.360001 | -0.866449 | 1.444644 | 9.000000 | 117.000000 | -648000000.000000 | 1980000000.000000 | 4.530000 | 437086092.700000 | 14.649007 | -4.044970 | 1 | |
| SWKS | Skyworks Solutions | Information Technology | Semiconductors | 76.830002 | -8.513933 | 2.017394 | 25.000000 | 225.000000 | 237800000.000000 | 798300000.000000 | 4.210000 | 189619952.500000 | 18.249407 | 7.413777 | 1 | |
| SWN | Southwestern Energy | Energy | Oil & Gas Exploration & Production | 7.110000 | -44.798137 | 4.580042 | 200.000000 | 2.000000 | -38000000.000000 | -4556000000.000000 | -6.070000 | 402141680.400000 | 93.089287 | 1.273530 | 1 | |
| SYF | Synchrony Financial | Financials | Consumer Finance | 30.410000 | -2.874478 | 1.835028 | 18.000000 | 99.000000 | 497000000.000000 | 2214000000.000000 | 2.660000 | 832330827.100000 | 11.432331 | -0.842213 | 1 | |
| SYK | Stryker Corp. | Health Care | Health Care Equipment | 92.940002 | -1.650792 | 1.138163 | 17.000000 | 116.000000 | 1584000000.000000 | 1439000000.000000 | 3.820000 | 376701570.700000 | 24.329843 | 7.026782 | 1 | |
| T | AT&T Inc | Telecommunications Services | Integrated Telecommunications Services | 34.410000 | 5.942118 | 0.859442 | 11.000000 | 11.000000 | -3482000000.000000 | 13345000000.000000 | 2.370000 | 5630801688.000000 | 14.518987 | -23.537323 | 1 | |
| TAP | Molson Coors Brewing Company | Consumer Staples | Brewers | 93.919998 | 13.129368 | 1.217803 | 5.000000 | 35.000000 | -150300000.000000 | 359500000.000000 | 1.940000 | 185309278.400000 | 48.412370 | -25.385129 | 1 | |
| TDC | Teradata Corp. | Information Technology | Application Software | 26.420000 | -8.833678 | 2.730659 | 25.000000 | 88.000000 | 5000000.000000 | -214000000.000000 | -1.530000 | 139869281.000000 | 74.555557 | 4.068084 | 1 | |
| TGNA | Tegna, Inc. | Consumer Discretionary | Publishing | 25.520000 | 13.624226 | 1.797269 | 21.000000 | 21.000000 | 10716000.000000 | 459522000.000000 | 2.040000 | 225255882.400000 | 12.509804 | -12.726553 | 1 | |
| TMK | Torchmark Corp. | Financials | Life & Health Insurance | 57.160000 | 1.168142 | 1.022968 | 13.000000 | 99.000000 | -4636000.000000 | 527100000.000000 | 4.210000 | 125201900.200000 | 13.577197 | -1.883912 | 1 | |
| TMO | Thermo Fisher Scientific | Health Care | Health Care Equipment | 141.850006 | 15.607180 | 1.247751 | 9.000000 | 11.000000 | -891400000.000000 | 1975400000.000000 | 4.960000 | 398266129.000000 | 28.598792 | -28.032512 | 1 | |
| TRIP | TripAdvisor | Consumer Discretionary | Internet & Direct Marketing Retail | 85.250000 | 34.803917 | 1.578344 | 14.000000 | 212.000000 | 159000000.000000 | 198000000.000000 | 1.380000 | 143478260.900000 | 61.775362 | 2.627576 | 1 | |
| TRV | The Travelers Companies Inc. | Financials | Property & Casualty Insurance | 112.860001 | 13.029548 | 0.959365 | 15.000000 | 99.000000 | 6000000.000000 | 3439000000.000000 | 10.990000 | 312920837.100000 | 10.269336 | -0.891599 | 1 | |
| TSCO | Tractor Supply Company | Consumer Discretionary | Specialty Retail | 85.500000 | 1.303315 | 1.431109 | 29.000000 | 10.000000 | 12679000.000000 | 410395000.000000 | 3.030000 | 135443894.400000 | 28.217822 | 6.010954 | 1 | |
| TSN | Tyson Foods | Consumer Staples | Packaged Foods & Meats | 53.330002 | 23.249369 | 1.586719 | 13.000000 | 19.000000 | 250000000.000000 | 1220000000.000000 | 3.280000 | 199237804.900000 | 24.070121 | -1.980483 | 1 | |
| TSO | Tesoro Petroleum Co. | Energy | Oil & Gas Refining & Marketing & Transportation | 105.370003 | 8.584091 | 1.854132 | 30.000000 | 37.000000 | -58000000.000000 | 1540000000.000000 | 12.500000 | 123200000.000000 | 8.429600 | 4.594156 | 1 | |
| TSS | Total System Services | Information Technology | Internet Software & Services | 49.799999 | 9.234479 | 1.579248 | 20.000000 | 115.000000 | 100145000.000000 | 369041000.000000 | 1.980000 | 186384343.400000 | 25.151515 | -2.347391 | 1 | |
| TXN | Texas Instruments | Information Technology | Semiconductors | 54.810001 | 9.971912 | 1.263479 | 30.000000 | 126.000000 | -199000000.000000 | 2986000000.000000 | 2.860000 | 1044055944.000000 | 19.164336 | 2.768051 | 1 | |
| UAA | Under Armour | Consumer Discretionary | Apparel, Accessories & Luxury Goods | 80.610001 | -16.948277 | 1.758824 | 14.000000 | 27.000000 | -463323000.000000 | 232573000.000000 | 3.030000 | 213598256.500000 | 20.819876 | -0.857290 | 1 | |
| UAL | United Continental Holdings | Industrials | Airlines | 57.299999 | 8.215294 | 1.747606 | 82.000000 | 42.000000 | 1004000000.000000 | 7340000000.000000 | 19.520000 | 376024590.200000 | 2.935451 | -23.195292 | 1 | |
| UDR | UDR Inc | Real Estate | Residential REITs | 37.570000 | 8.583821 | 1.157906 | 12.000000 | 47.000000 | -8482000.000000 | 340383000.000000 | 1.300000 | 261833076.900000 | 28.900000 | -3.089477 | 1 | |
| UHS | Universal Health Services, Inc. | Health Care | Health Care Facilities | 119.489998 | -5.136552 | 2.048697 | 16.000000 | 6.000000 | 29159000.000000 | 680528000.000000 | 6.890000 | 98770391.870000 | 17.342525 | 6.255903 | 1 | |
| UNH | United Health Group Inc. | Health Care | Managed Health Care | 117.639999 | 1.466273 | 1.482349 | 17.000000 | 70.000000 | 3428000000.000000 | 5813000000.000000 | 6.100000 | 952950819.700000 | 19.285246 | -8.805281 | 1 | |
| UNM | Unum Group | Financials | Diversified Financial Services | 33.290001 | 3.804181 | 1.102848 | 10.000000 | 117.000000 | 10400000.000000 | 867100000.000000 | 3.510000 | 247037037.000000 | 9.484331 | -4.178927 | 1 | |
| UNP | Union Pacific | Industrials | Railroads | 78.199997 | -12.371135 | 1.430297 | 23.000000 | 43.000000 | -195000000.000000 | 4772000000.000000 | 5.510000 | 866061706.000000 | 14.192377 | 1.066899 | 1 | |
| UPS | United Parcel Service | Industrials | Air Freight & Logistics | 96.230003 | -2.797977 | 0.826408 | 196.000000 | 44.000000 | 439000000.000000 | 4844000000.000000 | 5.380000 | 900371747.200000 | 17.886618 | 1.069558 | 1 | |
| UTX | United Technologies | Industrials | Industrial Conglomerates | 96.070000 | 8.065239 | 0.949396 | 28.000000 | 31.000000 | 1630000000.000000 | 7608000000.000000 | 8.720000 | 872477064.200000 | 11.017202 | -13.198055 | 1 | |
| VAR | Varian Medical Systems | Health Care | Health Care Equipment | 80.800003 | 9.248248 | 1.034843 | 24.000000 | 61.000000 | -3800000.000000 | 411500000.000000 | 4.130000 | 99636803.870000 | 19.564165 | 9.471400 | 1 | |
| VLO | Valero Energy | Energy | Oil & Gas Refining & Marketing & Transportation | 70.709999 | 17.341522 | 1.626934 | 19.000000 | 56.000000 | 425000000.000000 | 3990000000.000000 | 8.000000 | 498750000.000000 | 8.838750 | 15.262155 | 1 | |
| VMC | Vulcan Materials | Materials | Construction Materials | 94.970001 | 6.028805 | 1.845710 | 5.000000 | 81.000000 | 142787000.000000 | 221177000.000000 | 1.660000 | 133239156.600000 | 57.210844 | -0.266191 | 1 | |
| VNO | Vornado Realty Trust | Real Estate | REITs | 99.959999 | 10.027519 | 1.019724 | 11.000000 | 47.000000 | 637230000.000000 | 760434000.000000 | 3.610000 | 210646537.400000 | 27.689750 | -1.081912 | 1 | |
| VRSK | Verisk Analytics | Industrials | Research & Consulting Services | 76.879997 | -1.448539 | 1.454019 | 37.000000 | 10.000000 | 98989000.000000 | 507577000.000000 | 3.070000 | 165334527.700000 | 25.042344 | -13.617440 | 1 | |
| VRSN | Verisign Inc. | Information Technology | Internet Software & Services | 87.360001 | 23.459580 | 1.379480 | 35.000000 | 127.000000 | 37051000.000000 | 375236000.000000 | 3.290000 | 114053495.400000 | 26.553192 | 4.076543 | 1 | |
| VRTX | Vertex Pharmaceuticals Inc | Health Care | Biotechnology | 125.830002 | 21.928300 | 2.456535 | 59.000000 | 221.000000 | 89509000.000000 | -556334000.000000 | -2.310000 | 240837229.400000 | 39.602928 | 2.559671 | 1 | |
| VTR | Ventas Inc | Real Estate | REITs | 56.430000 | 0.213104 | 1.444924 | 4.000000 | 47.000000 | -1803000.000000 | 419222000.000000 | 1.260000 | 332715873.000000 | 44.785714 | -4.040751 | 1 | |
| VZ | Verizon Communications | Telecommunications Services | Integrated Telecommunications Services | 46.220001 | 6.277303 | 0.842592 | 109.000000 | 14.000000 | -6128000000.000000 | 17879000000.000000 | 4.380000 | 4081963470.000000 | 10.552512 | -26.380687 | 1 | |
| WAT | Waters Corporation | Health Care | Health Care Distributors | 134.580002 | 13.925341 | 1.044615 | 23.000000 | 425.000000 | 65488000.000000 | 469053000.000000 | 5.700000 | 82290000.000000 | 23.610527 | 29.547150 | 1 | |
| WEC | Wec Energy Group Inc | Utilities | Electric Utilities | 51.310001 | -1.986623 | 1.103033 | 7.000000 | 2.000000 | -12100000.000000 | 640300000.000000 | 2.360000 | 271313559.300000 | 21.741526 | -1.850995 | 1 | |
| WFC | Wells Fargo | Financials | Banks | 54.360001 | 5.532912 | 0.969774 | 12.000000 | 99.000000 | -460000000.000000 | 22894000000.000000 | 4.180000 | 5477033493.000000 | 13.004785 | -0.938007 | 1 | |
| WHR | Whirlpool Corp. | Consumer Discretionary | Household Appliances | 146.869995 | -0.230971 | 2.397803 | 17.000000 | 10.000000 | -254000000.000000 | 783000000.000000 | 9.950000 | 78693467.340000 | 14.760804 | -45.086335 | 1 | |
| WM | Waste Management Inc. | Industrials | Environmental Services | 53.369999 | 7.061186 | 0.940366 | 14.000000 | 2.000000 | -1268000000.000000 | 753000000.000000 | 1.660000 | 453614457.800000 | 32.150602 | -1.415299 | 1 | |
| WMB | Williams Cos. | Energy | Oil & Gas Exploration & Production | 25.700001 | -30.988186 | 3.719560 | 9.000000 | 4.000000 | -140000000.000000 | -571000000.000000 | -0.760000 | 751315789.500000 | 93.089287 | -14.561121 | 1 | |
| WU | Western Union Co | Information Technology | Internet Software & Services | 17.910000 | -2.610109 | 1.273051 | 60.000000 | 16.000000 | -467300000.000000 | 837800000.000000 | 1.630000 | 513987730.100000 | 10.987730 | -8.043772 | 1 | |
| WY | Weyerhaeuser Corp. | Real Estate | REITs | 29.980000 | 8.544529 | 1.338067 | 10.000000 | 116.000000 | -568000000.000000 | 506000000.000000 | 0.890000 | 568539325.800000 | 33.685393 | 2.284802 | 1 | |
| WYN | Wyndham Worldwide | Consumer Discretionary | Hotels, Resorts & Cruise Lines | 72.650002 | 1.000976 | 1.331918 | 64.000000 | 9.000000 | -12000000.000000 | 612000000.000000 | 5.180000 | 118146718.100000 | 14.025097 | -10.249967 | 1 | |
| WYNN | Wynn Resorts Ltd | Consumer Discretionary | Casinos & Gaming | 69.190002 | 29.496541 | 3.794783 | 174.000000 | 198.000000 | -102075000.000000 | 195290000.000000 | 1.930000 | 101186528.500000 | 35.849742 | 12.695712 | 1 | |
| XEC | Cimarex Energy | Energy | Oil & Gas Exploration & Production | 89.379997 | -14.403372 | 2.397940 | 86.000000 | 190.000000 | 373520000.000000 | -2408948000.000000 | -25.920000 | 92937808.640000 | 93.089287 | 7.186128 | 1 | |
| XEL | Xcel Energy Inc | Utilities | MultiUtilities | 35.910000 | 1.383405 | 1.015052 | 9.000000 | 2.000000 | 5332000.000000 | 984485000.000000 | 1.940000 | 507466494.800000 | 18.510309 | -2.261927 | 1 | |
| XL | XL Capital | Financials | Property & Casualty Insurance | 39.180000 | 7.696534 | 0.991011 | 10.000000 | 99.000000 | 734422000.000000 | 1201560000.000000 | 4.220000 | 284729857.800000 | 9.284360 | -7.762677 | 1 | |
| XOM | Exxon Mobil Corp. | Energy | Integrated Oil & Gas | 77.949997 | 3.656915 | 1.370062 | 9.000000 | 7.000000 | -911000000.000000 | 16150000000.000000 | 3.850000 | 4194805195.000000 | 20.246752 | -2.706443 | 1 | |
| XRAY | Dentsply Sirona | Health Care | Health Care Supplies | 60.849998 | 19.901474 | 1.007230 | 11.000000 | 60.000000 | 133000000.000000 | 251200000.000000 | 1.790000 | 140335195.500000 | 33.994412 | 0.855096 | 1 | |
| XRX | Xerox Corp. | Information Technology | IT Consulting & Other Services | 10.630000 | 9.474768 | 1.866680 | 5.000000 | 26.000000 | -43000000.000000 | 474000000.000000 | 0.420000 | 1128571429.000000 | 25.309524 | -0.295949 | 1 | |
| XYL | Xylem Inc. | Industrials | Industrial Conglomerates | 36.500000 | 11.009729 | 1.166311 | 16.000000 | 83.000000 | 17000000.000000 | 340000000.000000 | 1.880000 | 180851063.800000 | 19.414894 | 4.130471 | 1 | |
| YHOO | Yahoo Inc. | Information Technology | Internet Software & Services | 33.259998 | 14.887727 | 1.845149 | 15.000000 | 459.000000 | -1032187000.000000 | -4359082000.000000 | -4.640000 | 939457327.600000 | 28.976191 | 6.261775 | 1 | |
| YUM | Yum! Brands Inc | Consumer Discretionary | Restaurants | 52.516175 | -8.698917 | 1.478877 | 142.000000 | 27.000000 | 159000000.000000 | 1293000000.000000 | 2.970000 | 435353535.400000 | 17.682214 | -3.838260 | 1 | |
| ZBH | Zimmer Biomet Holdings | Health Care | Health Care Equipment | 102.589996 | 9.347683 | 1.404206 | 1.000000 | 100.000000 | 376000000.000000 | 147000000.000000 | 0.780000 | 188461538.500000 | 131.525636 | -23.884449 | 1 | |
| ZION | Zions Bancorp | Financials | Regional Banks | 27.299999 | -1.158588 | 1.468176 | 4.000000 | 99.000000 | -43623000.000000 | 309471000.000000 | 1.200000 | 257892500.000000 | 22.749999 | -0.063096 | 1 | |
| ZTS | Zoetis | Health Care | Pharmaceuticals | 47.919998 | 16.678836 | 1.610285 | 32.000000 | 65.000000 | 272000000.000000 | 339000000.000000 | 0.680000 | 498529411.800000 | 70.470585 | 1.723068 | 1 |
max_df=hc_cluster_profile.style.highlight_max(color="lightgreen", axis=0)
type(max_df)
pandas.io.formats.style.Styler
You can retrieve the original dataframe from the styler object using the "data" attribute.
In your example:
df = final_df.data
type(df) yields
pandas.core.frame.DataFrame
Is there a way to convert data frame styler object into dataframe in python
max_df_df=max_df.data
for col in max_df_df.columns:
# for i in max_df[col]:
print(col)
Current Price Price Change Volatility ROE Cash Ratio Net Cash Flow Net Income Earnings Per Share Estimated Shares Outstanding P/E Ratio P/B Ratio count_in_each_segment
type(max_df_df)
pandas.core.frame.DataFrame
# TODO
# Try to get max of each column and print it.
To find the maximum value of a column and to return its corresponding row values in Pandas, we can use:
df.loc[df[col].idxmax()]
Python Pandas – Find the maximum value of a column and return its corresponding row values%5D.)
max=max_df_df.loc[max_df_df[col].idxmax()]
max
Current Price 4.235000e+01 Price Change 9.999995e+00 Volatility 1.687151e+00 ROE 1.350000e+02 Cash Ratio 5.100000e+01 Net Cash Flow -6.040000e+08 Net Income 7.610000e+09 Earnings Per Share 1.139000e+01 Estimated Shares Outstanding 6.681299e+08 P/E Ratio 3.718174e+00 P/B Ratio -8.784219e+00 count_in_each_segment 1.000000e+00 Name: (0, AAL, American Airlines Group, Industrials, Airlines), dtype: float64
index_of_max=max_df_df[col].idxmax()
index_of_max
(0, 'AAL', 'American Airlines Group', 'Industrials', 'Airlines')
max=max_df_df.loc[max_df_df[col].idxmax()]
index_of_max=max_df_df[col].idxmax()
max_df_df.head(5)
| Current Price | Price Change | Volatility | ROE | Cash Ratio | Net Cash Flow | Net Income | Earnings Per Share | Estimated Shares Outstanding | P/E Ratio | P/B Ratio | count_in_each_segment | |||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| HC_segments | Ticker Symbol | Security | GICS Sector | GICS Sub Industry | ||||||||||||
| 0 | AAL | American Airlines Group | Industrials | Airlines | 42.349998 | 9.999995 | 1.687151 | 135.0 | 51.0 | -604000000.0 | 7.610000e+09 | 11.39 | 6.681299e+08 | 3.718174 | -8.784219 | 1 |
| ABBV | AbbVie | Health Care | Pharmaceuticals | 59.240002 | 8.339433 | 2.197887 | 130.0 | 77.0 | 51000000.0 | 5.144000e+09 | 3.15 | 1.633016e+09 | 18.806350 | -8.750068 | 1 | |
| ABT | Abbott Laboratories | Health Care | Health Care Equipment | 44.910000 | 11.301121 | 1.273646 | 21.0 | 67.0 | 938000000.0 | 4.423000e+09 | 2.94 | 1.504422e+09 | 15.275510 | -0.394171 | 1 | |
| ADBE | Adobe Systems Inc | Information Technology | Application Software | 93.940002 | 13.977195 | 1.357679 | 9.0 | 180.0 | -240840000.0 | 6.295510e+08 | 1.26 | 4.996437e+08 | 74.555557 | 4.199651 | 1 | |
| ADI | Analog Devices, Inc. | Information Technology | Semiconductors | 55.320000 | -1.827858 | 1.701169 | 14.0 | 272.0 | 315120000.0 | 6.968780e+08 | 0.31 | 2.247994e+09 | 178.451613 | 1.059810 | 1 |
for index,col in enumerate(max_df_df.columns):
print(col)
# print(max_df_df[col].idxmax())
print('index',index)
print('col',col)
# print(max_df_df[col][index])
Current Price index 0 col Current Price Price Change index 1 col Price Change Volatility index 2 col Volatility ROE index 3 col ROE Cash Ratio index 4 col Cash Ratio Net Cash Flow index 5 col Net Cash Flow Net Income index 6 col Net Income Earnings Per Share index 7 col Earnings Per Share Estimated Shares Outstanding index 8 col Estimated Shares Outstanding P/E Ratio index 9 col P/E Ratio P/B Ratio index 10 col P/B Ratio count_in_each_segment index 11 col count_in_each_segment
# max=max_df_df.loc[max_df_df[col].idxmax()]
# index_of_max=max_df_df[col].idxmax()
# max_df_df[col].idxmax()
# for i,val in enumerate(max_df_df[col]):
# if max_df_df[col][i] == max:
# if max_df_df[col][i] == 42.349998:
# print(max_df_df[col][i])
# print(max_df_df[col][i])
# print(val)
## Complete the code to print the companies in each cluster
for cl in df2['Ticker Symbol'].unique():
print("In cluster {}, the following companies are present:".format(cl))
print(df2[df2['Ticker Symbol'] == cl]["Security"].unique())
print()
In cluster AAL, the following companies are present: ['American Airlines Group'] In cluster ABBV, the following companies are present: ['AbbVie'] In cluster ABT, the following companies are present: ['Abbott Laboratories'] In cluster ADBE, the following companies are present: ['Adobe Systems Inc'] In cluster ADI, the following companies are present: ['Analog Devices, Inc.'] In cluster ADM, the following companies are present: ['Archer-Daniels-Midland Co'] In cluster ADS, the following companies are present: ['Alliance Data Systems'] In cluster AEE, the following companies are present: ['Ameren Corp'] In cluster AEP, the following companies are present: ['American Electric Power'] In cluster AFL, the following companies are present: ['AFLAC Inc'] In cluster AIG, the following companies are present: ['American International Group, Inc.'] In cluster AIV, the following companies are present: ['Apartment Investment & Mgmt'] In cluster AIZ, the following companies are present: ['Assurant Inc'] In cluster AJG, the following companies are present: ['Arthur J. Gallagher & Co.'] In cluster AKAM, the following companies are present: ['Akamai Technologies Inc'] In cluster ALB, the following companies are present: ['Albemarle Corp'] In cluster ALK, the following companies are present: ['Alaska Air Group Inc'] In cluster ALL, the following companies are present: ['Allstate Corp'] In cluster ALLE, the following companies are present: ['Allegion'] In cluster ALXN, the following companies are present: ['Alexion Pharmaceuticals'] In cluster AMAT, the following companies are present: ['Applied Materials Inc'] In cluster AME, the following companies are present: ['AMETEK Inc'] In cluster AMG, the following companies are present: ['Affiliated Managers Group Inc'] In cluster AMGN, the following companies are present: ['Amgen Inc'] In cluster AMP, the following companies are present: ['Ameriprise Financial'] In cluster AMT, the following companies are present: ['American Tower Corp A'] In cluster AMZN, the following companies are present: ['Amazon.com Inc'] In cluster AN, the following companies are present: ['AutoNation Inc'] In cluster ANTM, the following companies are present: ['Anthem Inc.'] In cluster AON, the following companies are present: ['Aon plc'] In cluster APA, the following companies are present: ['Apache Corporation'] In cluster APC, the following companies are present: ['Anadarko Petroleum Corp'] In cluster APH, the following companies are present: ['Amphenol Corp'] In cluster ARNC, the following companies are present: ['Arconic Inc'] In cluster ATVI, the following companies are present: ['Activision Blizzard'] In cluster AVB, the following companies are present: ['AvalonBay Communities, Inc.'] In cluster AVGO, the following companies are present: ['Broadcom'] In cluster AWK, the following companies are present: ['American Water Works Company Inc'] In cluster AXP, the following companies are present: ['American Express Co'] In cluster BA, the following companies are present: ['Boeing Company'] In cluster BAC, the following companies are present: ['Bank of America Corp'] In cluster BAX, the following companies are present: ['Baxter International Inc.'] In cluster BBT, the following companies are present: ['BB&T Corporation'] In cluster BCR, the following companies are present: ['Bard (C.R.) Inc.'] In cluster BHI, the following companies are present: ['Baker Hughes Inc'] In cluster BIIB, the following companies are present: ['BIOGEN IDEC Inc.'] In cluster BK, the following companies are present: ['The Bank of New York Mellon Corp.'] In cluster BLL, the following companies are present: ['Ball Corp'] In cluster BMY, the following companies are present: ['Bristol-Myers Squibb'] In cluster BSX, the following companies are present: ['Boston Scientific'] In cluster BWA, the following companies are present: ['BorgWarner'] In cluster BXP, the following companies are present: ['Boston Properties'] In cluster C, the following companies are present: ['Citigroup Inc.'] In cluster CAT, the following companies are present: ['Caterpillar Inc.'] In cluster CB, the following companies are present: ['Chubb Limited'] In cluster CBG, the following companies are present: ['CBRE Group'] In cluster CCI, the following companies are present: ['Crown Castle International Corp.'] In cluster CCL, the following companies are present: ['Carnival Corp.'] In cluster CELG, the following companies are present: ['Celgene Corp.'] In cluster CF, the following companies are present: ['CF Industries Holdings Inc'] In cluster CFG, the following companies are present: ['Citizens Financial Group'] In cluster CHD, the following companies are present: ['Church & Dwight'] In cluster CHK, the following companies are present: ['Chesapeake Energy'] In cluster CHRW, the following companies are present: ['C. H. Robinson Worldwide'] In cluster CHTR, the following companies are present: ['Charter Communications'] In cluster CI, the following companies are present: ['CIGNA Corp.'] In cluster CINF, the following companies are present: ['Cincinnati Financial'] In cluster CL, the following companies are present: ['Colgate-Palmolive'] In cluster CMA, the following companies are present: ['Comerica Inc.'] In cluster CME, the following companies are present: ['CME Group Inc.'] In cluster CMG, the following companies are present: ['Chipotle Mexican Grill'] In cluster CMI, the following companies are present: ['Cummins Inc.'] In cluster CMS, the following companies are present: ['CMS Energy'] In cluster CNC, the following companies are present: ['Centene Corporation'] In cluster CNP, the following companies are present: ['CenterPoint Energy'] In cluster COF, the following companies are present: ['Capital One Financial'] In cluster COG, the following companies are present: ['Cabot Oil & Gas'] In cluster COO, the following companies are present: ['The Cooper Companies'] In cluster CSX, the following companies are present: ['CSX Corp.'] In cluster CTL, the following companies are present: ['CenturyLink Inc'] In cluster CTSH, the following companies are present: ['Cognizant Technology Solutions'] In cluster CTXS, the following companies are present: ['Citrix Systems'] In cluster CVS, the following companies are present: ['CVS Health'] In cluster CVX, the following companies are present: ['Chevron Corp.'] In cluster CXO, the following companies are present: ['Concho Resources'] In cluster D, the following companies are present: ['Dominion Resources'] In cluster DAL, the following companies are present: ['Delta Air Lines'] In cluster DD, the following companies are present: ['Du Pont (E.I.)'] In cluster DE, the following companies are present: ['Deere & Co.'] In cluster DFS, the following companies are present: ['Discover Financial Services'] In cluster DGX, the following companies are present: ['Quest Diagnostics'] In cluster DHR, the following companies are present: ['Danaher Corp.'] In cluster DIS, the following companies are present: ['The Walt Disney Company'] In cluster DISCA, the following companies are present: ['Discovery Communications-A'] In cluster DISCK, the following companies are present: ['Discovery Communications-C'] In cluster DLPH, the following companies are present: ['Delphi Automotive'] In cluster DLR, the following companies are present: ['Digital Realty Trust'] In cluster DNB, the following companies are present: ['Dun & Bradstreet'] In cluster DOV, the following companies are present: ['Dover Corp.'] In cluster DPS, the following companies are present: ['Dr Pepper Snapple Group'] In cluster DUK, the following companies are present: ['Duke Energy'] In cluster DVA, the following companies are present: ['DaVita Inc.'] In cluster DVN, the following companies are present: ['Devon Energy Corp.'] In cluster EBAY, the following companies are present: ['eBay Inc.'] In cluster ECL, the following companies are present: ['Ecolab Inc.'] In cluster ED, the following companies are present: ['Consolidated Edison'] In cluster EFX, the following companies are present: ['Equifax Inc.'] In cluster EIX, the following companies are present: ["Edison Int'l"] In cluster EMN, the following companies are present: ['Eastman Chemical'] In cluster EOG, the following companies are present: ['EOG Resources'] In cluster EQIX, the following companies are present: ['Equinix'] In cluster EQR, the following companies are present: ['Equity Residential'] In cluster EQT, the following companies are present: ['EQT Corporation'] In cluster ES, the following companies are present: ['Eversource Energy'] In cluster ESS, the following companies are present: ['Essex Property Trust, Inc.'] In cluster ETFC, the following companies are present: ['E*Trade'] In cluster ETN, the following companies are present: ['Eaton Corporation'] In cluster ETR, the following companies are present: ['Entergy Corp.'] In cluster EW, the following companies are present: ['Edwards Lifesciences'] In cluster EXC, the following companies are present: ['Exelon Corp.'] In cluster EXPD, the following companies are present: ["Expeditors Int'l"] In cluster EXPE, the following companies are present: ['Expedia Inc.'] In cluster EXR, the following companies are present: ['Extra Space Storage'] In cluster F, the following companies are present: ['Ford Motor'] In cluster FAST, the following companies are present: ['Fastenal Co'] In cluster FB, the following companies are present: ['Facebook'] In cluster FBHS, the following companies are present: ['Fortune Brands Home & Security'] In cluster FCX, the following companies are present: ['Freeport-McMoran Cp & Gld'] In cluster FE, the following companies are present: ['FirstEnergy Corp'] In cluster FIS, the following companies are present: ['Fidelity National Information Services'] In cluster FISV, the following companies are present: ['Fiserv Inc'] In cluster FLIR, the following companies are present: ['FLIR Systems'] In cluster FLR, the following companies are present: ['Fluor Corp.'] In cluster FLS, the following companies are present: ['Flowserve Corporation'] In cluster FMC, the following companies are present: ['FMC Corporation'] In cluster FRT, the following companies are present: ['Federal Realty Investment Trust'] In cluster FSLR, the following companies are present: ['First Solar Inc'] In cluster FTR, the following companies are present: ['Frontier Communications'] In cluster GD, the following companies are present: ['General Dynamics'] In cluster GGP, the following companies are present: ['General Growth Properties Inc.'] In cluster GILD, the following companies are present: ['Gilead Sciences'] In cluster GLW, the following companies are present: ['Corning Inc.'] In cluster GM, the following companies are present: ['General Motors'] In cluster GPC, the following companies are present: ['Genuine Parts'] In cluster GRMN, the following companies are present: ['Garmin Ltd.'] In cluster GT, the following companies are present: ['Goodyear Tire & Rubber'] In cluster GWW, the following companies are present: ['Grainger (W.W.) Inc.'] In cluster HAL, the following companies are present: ['Halliburton Co.'] In cluster HAS, the following companies are present: ['Hasbro Inc.'] In cluster HBAN, the following companies are present: ['Huntington Bancshares'] In cluster HCA, the following companies are present: ['HCA Holdings'] In cluster HCN, the following companies are present: ['Welltower Inc.'] In cluster HCP, the following companies are present: ['HCP Inc.'] In cluster HES, the following companies are present: ['Hess Corporation'] In cluster HIG, the following companies are present: ['Hartford Financial Svc.Gp.'] In cluster HOG, the following companies are present: ['Harley-Davidson'] In cluster HON, the following companies are present: ["Honeywell Int'l Inc."] In cluster HPE, the following companies are present: ['Hewlett Packard Enterprise'] In cluster HPQ, the following companies are present: ['HP Inc.'] In cluster HRL, the following companies are present: ['Hormel Foods Corp.'] In cluster HSIC, the following companies are present: ['Henry Schein'] In cluster HST, the following companies are present: ['Host Hotels & Resorts'] In cluster HSY, the following companies are present: ['The Hershey Company'] In cluster HUM, the following companies are present: ['Humana Inc.'] In cluster IBM, the following companies are present: ['International Business Machines'] In cluster IDXX, the following companies are present: ['IDEXX Laboratories'] In cluster IFF, the following companies are present: ['Intl Flavors & Fragrances'] In cluster INTC, the following companies are present: ['Intel Corp.'] In cluster IP, the following companies are present: ['International Paper'] In cluster IPG, the following companies are present: ['Interpublic Group'] In cluster IRM, the following companies are present: ['Iron Mountain Incorporated'] In cluster ISRG, the following companies are present: ['Intuitive Surgical Inc.'] In cluster ITW, the following companies are present: ['Illinois Tool Works'] In cluster IVZ, the following companies are present: ['Invesco Ltd.'] In cluster JBHT, the following companies are present: ['J. B. Hunt Transport Services'] In cluster JEC, the following companies are present: ['Jacobs Engineering Group'] In cluster JNPR, the following companies are present: ['Juniper Networks'] In cluster JPM, the following companies are present: ['JPMorgan Chase & Co.'] In cluster KIM, the following companies are present: ['Kimco Realty'] In cluster KMB, the following companies are present: ['Kimberly-Clark'] In cluster KMI, the following companies are present: ['Kinder Morgan'] In cluster KO, the following companies are present: ['Coca Cola Company'] In cluster KSU, the following companies are present: ['Kansas City Southern'] In cluster LEG, the following companies are present: ['Leggett & Platt'] In cluster LEN, the following companies are present: ['Lennar Corp.'] In cluster LH, the following companies are present: ['Laboratory Corp. of America Holding'] In cluster LKQ, the following companies are present: ['LKQ Corporation'] In cluster LLL, the following companies are present: ['L-3 Communications Holdings'] In cluster LLY, the following companies are present: ['Lilly (Eli) & Co.'] In cluster LMT, the following companies are present: ['Lockheed Martin Corp.'] In cluster LNT, the following companies are present: ['Alliant Energy Corp'] In cluster LUK, the following companies are present: ['Leucadia National Corp.'] In cluster LUV, the following companies are present: ['Southwest Airlines'] In cluster LVLT, the following companies are present: ['Level 3 Communications'] In cluster LYB, the following companies are present: ['LyondellBasell'] In cluster MA, the following companies are present: ['Mastercard Inc.'] In cluster MAA, the following companies are present: ['Mid-America Apartments'] In cluster MAC, the following companies are present: ['Macerich'] In cluster MAR, the following companies are present: ["Marriott Int'l."] In cluster MAS, the following companies are present: ['Masco Corp.'] In cluster MAT, the following companies are present: ['Mattel Inc.'] In cluster MCD, the following companies are present: ["McDonald's Corp."] In cluster MCO, the following companies are present: ["Moody's Corp"] In cluster MDLZ, the following companies are present: ['Mondelez International'] In cluster MET, the following companies are present: ['MetLife Inc.'] In cluster MHK, the following companies are present: ['Mohawk Industries'] In cluster MJN, the following companies are present: ['Mead Johnson'] In cluster MKC, the following companies are present: ['McCormick & Co.'] In cluster MLM, the following companies are present: ['Martin Marietta Materials'] In cluster MMC, the following companies are present: ['Marsh & McLennan'] In cluster MMM, the following companies are present: ['3M Company'] In cluster MNST, the following companies are present: ['Monster Beverage'] In cluster MO, the following companies are present: ['Altria Group Inc'] In cluster MOS, the following companies are present: ['The Mosaic Company'] In cluster MPC, the following companies are present: ['Marathon Petroleum'] In cluster MRK, the following companies are present: ['Merck & Co.'] In cluster MRO, the following companies are present: ['Marathon Oil Corp.'] In cluster MTB, the following companies are present: ['M&T Bank Corp.'] In cluster MTD, the following companies are present: ['Mettler Toledo'] In cluster MUR, the following companies are present: ['Murphy Oil'] In cluster MYL, the following companies are present: ['Mylan N.V.'] In cluster NAVI, the following companies are present: ['Navient'] In cluster NBL, the following companies are present: ['Noble Energy Inc'] In cluster NDAQ, the following companies are present: ['NASDAQ OMX Group'] In cluster NEE, the following companies are present: ['NextEra Energy'] In cluster NEM, the following companies are present: ['Newmont Mining Corp. (Hldg. Co.)'] In cluster NFLX, the following companies are present: ['Netflix Inc.'] In cluster NFX, the following companies are present: ['Newfield Exploration Co'] In cluster NLSN, the following companies are present: ['Nielsen Holdings'] In cluster NOV, the following companies are present: ['National Oilwell Varco Inc.'] In cluster NSC, the following companies are present: ['Norfolk Southern Corp.'] In cluster NTRS, the following companies are present: ['Northern Trust Corp.'] In cluster NUE, the following companies are present: ['Nucor Corp.'] In cluster NWL, the following companies are present: ['Newell Brands'] In cluster O, the following companies are present: ['Realty Income Corporation'] In cluster OKE, the following companies are present: ['ONEOK'] In cluster OMC, the following companies are present: ['Omnicom Group'] In cluster ORLY, the following companies are present: ["O'Reilly Automotive"] In cluster OXY, the following companies are present: ['Occidental Petroleum'] In cluster PBCT, the following companies are present: ["People's United Financial"] In cluster PBI, the following companies are present: ['Pitney-Bowes'] In cluster PCAR, the following companies are present: ['PACCAR Inc.'] In cluster PCG, the following companies are present: ['PG&E Corp.'] In cluster PCLN, the following companies are present: ['Priceline.com Inc'] In cluster PEG, the following companies are present: ['Public Serv. Enterprise Inc.'] In cluster PEP, the following companies are present: ['PepsiCo Inc.'] In cluster PFE, the following companies are present: ['Pfizer Inc.'] In cluster PFG, the following companies are present: ['Principal Financial Group'] In cluster PG, the following companies are present: ['Procter & Gamble'] In cluster PGR, the following companies are present: ['Progressive Corp.'] In cluster PHM, the following companies are present: ['Pulte Homes Inc.'] In cluster PM, the following companies are present: ['Philip Morris International'] In cluster PNC, the following companies are present: ['PNC Financial Services'] In cluster PNR, the following companies are present: ['Pentair Ltd.'] In cluster PNW, the following companies are present: ['Pinnacle West Capital'] In cluster PPG, the following companies are present: ['PPG Industries'] In cluster PPL, the following companies are present: ['PPL Corp.'] In cluster PRU, the following companies are present: ['Prudential Financial'] In cluster PSX, the following companies are present: ['Phillips 66'] In cluster PWR, the following companies are present: ['Quanta Services Inc.'] In cluster PX, the following companies are present: ['Praxair Inc.'] In cluster PYPL, the following companies are present: ['PayPal'] In cluster R, the following companies are present: ['Ryder System'] In cluster RCL, the following companies are present: ['Royal Caribbean Cruises Ltd'] In cluster REGN, the following companies are present: ['Regeneron'] In cluster RHI, the following companies are present: ['Robert Half International'] In cluster ROP, the following companies are present: ['Roper Industries'] In cluster RRC, the following companies are present: ['Range Resources Corp.'] In cluster RSG, the following companies are present: ['Republic Services Inc'] In cluster SCG, the following companies are present: ['SCANA Corp'] In cluster SCHW, the following companies are present: ['Charles Schwab Corporation'] In cluster SE, the following companies are present: ['Spectra Energy Corp.'] In cluster SEE, the following companies are present: ['Sealed Air'] In cluster SHW, the following companies are present: ['Sherwin-Williams'] In cluster SLG, the following companies are present: ['SL Green Realty'] In cluster SNI, the following companies are present: ['Scripps Networks Interactive Inc.'] In cluster SO, the following companies are present: ['Southern Co.'] In cluster SPG, the following companies are present: ['Simon Property Group Inc'] In cluster SPGI, the following companies are present: ['S&P Global, Inc.'] In cluster SRCL, the following companies are present: ['Stericycle Inc'] In cluster SRE, the following companies are present: ['Sempra Energy'] In cluster STI, the following companies are present: ['SunTrust Banks'] In cluster STT, the following companies are present: ['State Street Corp.'] In cluster SWKS, the following companies are present: ['Skyworks Solutions'] In cluster SWN, the following companies are present: ['Southwestern Energy'] In cluster SYF, the following companies are present: ['Synchrony Financial'] In cluster SYK, the following companies are present: ['Stryker Corp.'] In cluster T, the following companies are present: ['AT&T Inc'] In cluster TAP, the following companies are present: ['Molson Coors Brewing Company'] In cluster TDC, the following companies are present: ['Teradata Corp.'] In cluster TGNA, the following companies are present: ['Tegna, Inc.'] In cluster TMK, the following companies are present: ['Torchmark Corp.'] In cluster TMO, the following companies are present: ['Thermo Fisher Scientific'] In cluster TRIP, the following companies are present: ['TripAdvisor'] In cluster TRV, the following companies are present: ['The Travelers Companies Inc.'] In cluster TSCO, the following companies are present: ['Tractor Supply Company'] In cluster TSN, the following companies are present: ['Tyson Foods'] In cluster TSO, the following companies are present: ['Tesoro Petroleum Co.'] In cluster TSS, the following companies are present: ['Total System Services'] In cluster TXN, the following companies are present: ['Texas Instruments'] In cluster UAA, the following companies are present: ['Under Armour'] In cluster UAL, the following companies are present: ['United Continental Holdings'] In cluster UDR, the following companies are present: ['UDR Inc'] In cluster UHS, the following companies are present: ['Universal Health Services, Inc.'] In cluster UNH, the following companies are present: ['United Health Group Inc.'] In cluster UNM, the following companies are present: ['Unum Group'] In cluster UNP, the following companies are present: ['Union Pacific'] In cluster UPS, the following companies are present: ['United Parcel Service'] In cluster UTX, the following companies are present: ['United Technologies'] In cluster VAR, the following companies are present: ['Varian Medical Systems'] In cluster VLO, the following companies are present: ['Valero Energy'] In cluster VMC, the following companies are present: ['Vulcan Materials'] In cluster VNO, the following companies are present: ['Vornado Realty Trust'] In cluster VRSK, the following companies are present: ['Verisk Analytics'] In cluster VRSN, the following companies are present: ['Verisign Inc.'] In cluster VRTX, the following companies are present: ['Vertex Pharmaceuticals Inc'] In cluster VTR, the following companies are present: ['Ventas Inc'] In cluster VZ, the following companies are present: ['Verizon Communications'] In cluster WAT, the following companies are present: ['Waters Corporation'] In cluster WEC, the following companies are present: ['Wec Energy Group Inc'] In cluster WFC, the following companies are present: ['Wells Fargo'] In cluster WHR, the following companies are present: ['Whirlpool Corp.'] In cluster WM, the following companies are present: ['Waste Management Inc.'] In cluster WMB, the following companies are present: ['Williams Cos.'] In cluster WU, the following companies are present: ['Western Union Co'] In cluster WY, the following companies are present: ['Weyerhaeuser Corp.'] In cluster WYN, the following companies are present: ['Wyndham Worldwide'] In cluster WYNN, the following companies are present: ['Wynn Resorts Ltd'] In cluster XEC, the following companies are present: ['Cimarex Energy'] In cluster XEL, the following companies are present: ['Xcel Energy Inc'] In cluster XL, the following companies are present: ['XL Capital'] In cluster XOM, the following companies are present: ['Exxon Mobil Corp.'] In cluster XRAY, the following companies are present: ['Dentsply Sirona'] In cluster XRX, the following companies are present: ['Xerox Corp.'] In cluster XYL, the following companies are present: ['Xylem Inc.'] In cluster YHOO, the following companies are present: ['Yahoo Inc.'] In cluster YUM, the following companies are present: ['Yum! Brands Inc'] In cluster ZBH, the following companies are present: ['Zimmer Biomet Holdings'] In cluster ZION, the following companies are present: ['Zions Bancorp'] In cluster ZTS, the following companies are present: ['Zoetis']
Edit
the following cell
to print
df2[df2['GICS Sub Industry'] == c1]["Security"].unique()
Cluster / Group by 'GICS Sub Industry':
## Failed Attempt to group by df2['GICS Sub Industry'].unique()
## Duplicated from previous cell:
## Complete the code to print the companies in each cluster
for cl in df2['GICS Sub Industry'].unique():
print("In cluster {}, the following companies are present:".format(cl))
print(df2[df2['GICS Sub Industry'] == cl]["Security"].unique())
print()
In cluster Airlines, the following companies are present: ['American Airlines Group' 'Alaska Air Group Inc' 'Delta Air Lines' 'Southwest Airlines' 'United Continental Holdings'] In cluster Pharmaceuticals, the following companies are present: ['AbbVie' 'Lilly (Eli) & Co.' 'Merck & Co.' 'Mylan N.V.' 'Pfizer Inc.' 'Zoetis'] In cluster Health Care Equipment, the following companies are present: ['Abbott Laboratories' 'Baxter International Inc.' 'Bard (C.R.) Inc.' 'Boston Scientific' 'Edwards Lifesciences' 'IDEXX Laboratories' 'Intuitive Surgical Inc.' 'Stryker Corp.' 'Thermo Fisher Scientific' 'Varian Medical Systems' 'Zimmer Biomet Holdings'] In cluster Application Software, the following companies are present: ['Adobe Systems Inc' 'Teradata Corp.'] In cluster Semiconductors, the following companies are present: ['Analog Devices, Inc.' 'Broadcom' 'First Solar Inc' 'Intel Corp.' 'Skyworks Solutions' 'Texas Instruments'] In cluster Agricultural Products, the following companies are present: ['Archer-Daniels-Midland Co'] In cluster Data Processing & Outsourced Services, the following companies are present: ['Alliance Data Systems' 'PayPal'] In cluster MultiUtilities, the following companies are present: ['Ameren Corp' 'CMS Energy' 'CenterPoint Energy' 'Eversource Energy' 'Exelon Corp.' 'NextEra Energy' 'PG&E Corp.' 'Pinnacle West Capital' 'SCANA Corp' 'Sempra Energy' 'Xcel Energy Inc'] In cluster Electric Utilities, the following companies are present: ['American Electric Power' 'Dominion Resources' 'Duke Energy' 'Consolidated Edison' "Edison Int'l" 'Entergy Corp.' 'FirstEnergy Corp' 'Alliant Energy Corp' 'Public Serv. Enterprise Inc.' 'PPL Corp.' 'Southern Co.' 'Wec Energy Group Inc'] In cluster Life & Health Insurance, the following companies are present: ['AFLAC Inc' 'MetLife Inc.' 'Torchmark Corp.'] In cluster Property & Casualty Insurance, the following companies are present: ['American International Group, Inc.' 'Allstate Corp' 'Chubb Limited' 'Cincinnati Financial' 'Hartford Financial Svc.Gp.' 'Progressive Corp.' 'The Travelers Companies Inc.' 'XL Capital'] In cluster REITs, the following companies are present: ['Apartment Investment & Mgmt' 'Boston Properties' 'Crown Castle International Corp.' 'Equinix' 'Equity Residential' 'Welltower Inc.' 'HCP Inc.' 'Host Hotels & Resorts' 'Iron Mountain Incorporated' 'Kimco Realty' 'Simon Property Group Inc' 'Vornado Realty Trust' 'Ventas Inc' 'Weyerhaeuser Corp.'] In cluster Multi-line Insurance, the following companies are present: ['Assurant Inc'] In cluster Insurance Brokers, the following companies are present: ['Arthur J. Gallagher & Co.' 'Aon plc' 'Marsh & McLennan'] In cluster Internet Software & Services, the following companies are present: ['Akamai Technologies Inc' 'Citrix Systems' 'eBay Inc.' 'Facebook' 'Fidelity National Information Services' 'Fiserv Inc' 'Mastercard Inc.' 'Netflix Inc.' 'Total System Services' 'Verisign Inc.' 'Western Union Co' 'Yahoo Inc.'] In cluster Specialty Chemicals, the following companies are present: ['Albemarle Corp' 'Ecolab Inc.' 'Intl Flavors & Fragrances' 'Sherwin-Williams'] In cluster Building Products, the following companies are present: ['Allegion' 'Fastenal Co' 'Fortune Brands Home & Security' 'Masco Corp.'] In cluster Biotechnology, the following companies are present: ['Alexion Pharmaceuticals' 'Amgen Inc' 'BIOGEN IDEC Inc.' 'Celgene Corp.' 'Gilead Sciences' 'Regeneron' 'Vertex Pharmaceuticals Inc'] In cluster Semiconductor Equipment, the following companies are present: ['Applied Materials Inc'] In cluster Electrical Components & Equipment, the following companies are present: ['AMETEK Inc'] In cluster Asset Management & Custody Banks, the following companies are present: ['Affiliated Managers Group Inc' 'Ameriprise Financial' 'Invesco Ltd.' 'Northern Trust Corp.'] In cluster Specialized REITs, the following companies are present: ['American Tower Corp A' 'Digital Realty Trust' 'Extra Space Storage'] In cluster Internet & Direct Marketing Retail, the following companies are present: ['Amazon.com Inc' 'Expedia Inc.' 'Priceline.com Inc' 'TripAdvisor'] In cluster Specialty Stores, the following companies are present: ['AutoNation Inc' 'Genuine Parts' "O'Reilly Automotive"] In cluster Managed Health Care, the following companies are present: ['Anthem Inc.' 'CIGNA Corp.' 'Centene Corporation' 'Humana Inc.' 'United Health Group Inc.'] In cluster Oil & Gas Exploration & Production, the following companies are present: ['Apache Corporation' 'Anadarko Petroleum Corp' 'Cabot Oil & Gas' 'Concho Resources' 'Devon Energy Corp.' 'EOG Resources' 'EQT Corporation' 'Marathon Oil Corp.' 'Noble Energy Inc' 'Newfield Exploration Co' 'ONEOK' 'Occidental Petroleum' 'Range Resources Corp.' 'Southwestern Energy' 'Williams Cos.' 'Cimarex Energy'] In cluster Electronic Components, the following companies are present: ['Amphenol Corp' 'Corning Inc.'] In cluster Aerospace & Defense, the following companies are present: ['Arconic Inc' 'Boeing Company' 'General Dynamics' 'Lockheed Martin Corp.'] In cluster Home Entertainment Software, the following companies are present: ['Activision Blizzard'] In cluster Residential REITs, the following companies are present: ['AvalonBay Communities, Inc.' 'Essex Property Trust, Inc.' 'Mid-America Apartments' 'UDR Inc'] In cluster Water Utilities, the following companies are present: ['American Water Works Company Inc'] In cluster Consumer Finance, the following companies are present: ['American Express Co' 'Capital One Financial' 'Discover Financial Services' 'Navient' 'Synchrony Financial'] In cluster Banks, the following companies are present: ['Bank of America Corp' 'BB&T Corporation' 'The Bank of New York Mellon Corp.' 'Citigroup Inc.' 'Huntington Bancshares' 'JPMorgan Chase & Co.' 'M&T Bank Corp.' 'PNC Financial Services' 'SunTrust Banks' 'Wells Fargo'] In cluster Oil & Gas Equipment & Services, the following companies are present: ['Baker Hughes Inc' 'Halliburton Co.' 'National Oilwell Varco Inc.'] In cluster Metal & Glass Containers, the following companies are present: ['Ball Corp'] In cluster Health Care Distributors, the following companies are present: ['Bristol-Myers Squibb' 'Henry Schein' 'Waters Corporation'] In cluster Auto Parts & Equipment, the following companies are present: ['BorgWarner' 'Delphi Automotive'] In cluster Construction & Farm Machinery & Heavy Trucks, the following companies are present: ['Caterpillar Inc.' 'Deere & Co.' 'PACCAR Inc.'] In cluster Real Estate Services, the following companies are present: ['CBRE Group'] In cluster Hotels, Resorts & Cruise Lines, the following companies are present: ['Carnival Corp.' "Marriott Int'l." 'Royal Caribbean Cruises Ltd' 'Wyndham Worldwide'] In cluster Fertilizers & Agricultural Chemicals, the following companies are present: ['CF Industries Holdings Inc' 'The Mosaic Company'] In cluster Regional Banks, the following companies are present: ['Citizens Financial Group' 'Comerica Inc.' 'Zions Bancorp'] In cluster Household Products, the following companies are present: ['Church & Dwight' 'Colgate-Palmolive' 'Kimberly-Clark'] In cluster Integrated Oil & Gas, the following companies are present: ['Chesapeake Energy' 'Chevron Corp.' 'Hess Corporation' 'Murphy Oil' 'Exxon Mobil Corp.'] In cluster Air Freight & Logistics, the following companies are present: ['C. H. Robinson Worldwide' "Expeditors Int'l" 'United Parcel Service'] In cluster Cable & Satellite, the following companies are present: ['Charter Communications' 'Discovery Communications-A' 'Discovery Communications-C'] In cluster Financial Exchanges & Data, the following companies are present: ['CME Group Inc.'] In cluster Restaurants, the following companies are present: ['Chipotle Mexican Grill' "McDonald's Corp." 'Yum! Brands Inc'] In cluster Industrial Machinery, the following companies are present: ['Cummins Inc.' 'Dover Corp.' 'Flowserve Corporation' 'Illinois Tool Works' 'Pentair Ltd.'] In cluster Health Care Supplies, the following companies are present: ['The Cooper Companies' 'Dentsply Sirona'] In cluster Railroads, the following companies are present: ['CSX Corp.' 'Kansas City Southern' 'Norfolk Southern Corp.' 'Union Pacific'] In cluster Integrated Telecommunications Services, the following companies are present: ['CenturyLink Inc' 'Frontier Communications' 'AT&T Inc' 'Verizon Communications'] In cluster IT Consulting & Other Services, the following companies are present: ['Cognizant Technology Solutions' 'International Business Machines' 'Xerox Corp.'] In cluster Drug Retail, the following companies are present: ['CVS Health'] In cluster Diversified Chemicals, the following companies are present: ['Du Pont (E.I.)' 'Eastman Chemical' 'FMC Corporation' 'LyondellBasell' 'PPG Industries'] In cluster Health Care Facilities, the following companies are present: ['Quest Diagnostics' 'DaVita Inc.' 'HCA Holdings' 'Laboratory Corp. of America Holding' 'Universal Health Services, Inc.'] In cluster Industrial Conglomerates, the following companies are present: ['Danaher Corp.' 'Eaton Corporation' "Honeywell Int'l Inc." 'Jacobs Engineering Group' 'Leggett & Platt' 'L-3 Communications Holdings' '3M Company' 'Quanta Services Inc.' 'Ryder System' 'Roper Industries' 'Republic Services Inc' 'Stericycle Inc' 'United Technologies' 'Xylem Inc.'] In cluster Broadcasting & Cable TV, the following companies are present: ['The Walt Disney Company' 'Scripps Networks Interactive Inc.'] In cluster Research & Consulting Services, the following companies are present: ['Dun & Bradstreet' 'Equifax Inc.' 'Nielsen Holdings' 'Verisk Analytics'] In cluster Soft Drinks, the following companies are present: ['Dr Pepper Snapple Group' 'Coca Cola Company' 'Monster Beverage' 'PepsiCo Inc.'] In cluster Investment Banking & Brokerage, the following companies are present: ['E*Trade' 'Charles Schwab Corporation'] In cluster Automobile Manufacturers, the following companies are present: ['Ford Motor' 'General Motors'] In cluster Copper, the following companies are present: ['Freeport-McMoran Cp & Gld'] In cluster Electronic Equipment & Instruments, the following companies are present: ['FLIR Systems'] In cluster Diversified Commercial Services, the following companies are present: ['Fluor Corp.'] In cluster Retail REITs, the following companies are present: ['Federal Realty Investment Trust' 'General Growth Properties Inc.' 'Macerich' 'Realty Income Corporation'] In cluster Consumer Electronics, the following companies are present: ['Garmin Ltd.'] In cluster Tires & Rubber, the following companies are present: ['Goodyear Tire & Rubber'] In cluster Industrial Materials, the following companies are present: ['Grainger (W.W.) Inc.'] In cluster Leisure Products, the following companies are present: ['Hasbro Inc.' 'Mattel Inc.'] In cluster Motorcycle Manufacturers, the following companies are present: ['Harley-Davidson'] In cluster Technology Hardware, Storage & Peripherals, the following companies are present: ['Hewlett Packard Enterprise'] In cluster Computer Hardware, the following companies are present: ['HP Inc.'] In cluster Packaged Foods & Meats, the following companies are present: ['Hormel Foods Corp.' 'The Hershey Company' 'Mondelez International' 'Mead Johnson' 'McCormick & Co.' 'Tyson Foods'] In cluster Paper Packaging, the following companies are present: ['International Paper' 'Sealed Air'] In cluster Advertising, the following companies are present: ['Interpublic Group' 'Omnicom Group'] In cluster Trucking, the following companies are present: ['J. B. Hunt Transport Services'] In cluster Networking Equipment, the following companies are present: ['Juniper Networks'] In cluster Oil & Gas Refining & Marketing & Transportation, the following companies are present: ['Kinder Morgan' 'Marathon Petroleum' 'Phillips 66' 'Spectra Energy Corp.' 'Tesoro Petroleum Co.' 'Valero Energy'] In cluster Homebuilding, the following companies are present: ['Lennar Corp.' 'Pulte Homes Inc.'] In cluster Distributors, the following companies are present: ['LKQ Corporation'] In cluster Multi-Sector Holdings, the following companies are present: ['Leucadia National Corp.'] In cluster Alternative Carriers, the following companies are present: ['Level 3 Communications'] In cluster Diversified Financial Services, the following companies are present: ["Moody's Corp" 'NASDAQ OMX Group' 'Principal Financial Group' 'Prudential Financial' 'S&P Global, Inc.' 'State Street Corp.' 'Unum Group'] In cluster Home Furnishings, the following companies are present: ['Mohawk Industries'] In cluster Construction Materials, the following companies are present: ['Martin Marietta Materials' 'Vulcan Materials'] In cluster Tobacco, the following companies are present: ['Altria Group Inc' 'Philip Morris International'] In cluster Life Sciences Tools & Services, the following companies are present: ['Mettler Toledo'] In cluster Gold, the following companies are present: ['Newmont Mining Corp. (Hldg. Co.)'] In cluster Steel, the following companies are present: ['Nucor Corp.'] In cluster Housewares & Specialties, the following companies are present: ['Newell Brands'] In cluster Thrifts & Mortgage Finance, the following companies are present: ["People's United Financial"] In cluster Technology, Hardware, Software and Supplies, the following companies are present: ['Pitney-Bowes'] In cluster Personal Products, the following companies are present: ['Procter & Gamble'] In cluster Industrial Gases, the following companies are present: ['Praxair Inc.'] In cluster Human Resource & Employment Services, the following companies are present: ['Robert Half International'] In cluster Office REITs, the following companies are present: ['SL Green Realty'] In cluster Brewers, the following companies are present: ['Molson Coors Brewing Company'] In cluster Publishing, the following companies are present: ['Tegna, Inc.'] In cluster Specialty Retail, the following companies are present: ['Tractor Supply Company'] In cluster Apparel, Accessories & Luxury Goods, the following companies are present: ['Under Armour'] In cluster Household Appliances, the following companies are present: ['Whirlpool Corp.'] In cluster Environmental Services, the following companies are present: ['Waste Management Inc.'] In cluster Casinos & Gaming, the following companies are present: ['Wynn Resorts Ltd']
df2.groupby(["HC_segments", "GICS Sector"])['Security'].count()
HC_segments GICS Sector
0 Consumer Discretionary 40
Consumer Staples 19
Energy 30
Financials 49
Health Care 40
Industrials 53
Information Technology 33
Materials 20
Real Estate 27
Telecommunications Services 5
Utilities 24
Name: Security, dtype: int64
ax = plt.subplot(111, xlabel='x', ylabel='y', title='title')
for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +
ax.get_xticklabels() + ax.get_yticklabels()):
item.set_fontsize(20)
plt.gcf().number How do you get the current figure number in Python's matplotlib?
plt.gcf().number
1
<Figure size 800x550 with 0 Axes>
plt.get_fignums()
[]
allaxes = fig.get_axes()
allaxes
[<Axes: title={'center': 'Dendrogram (Single Linkage)'}>,
<Axes: title={'center': 'Dendrogram (Complete Linkage)'}>,
<Axes: title={'center': 'Dendrogram (Average Linkage)'}>,
<Axes: title={'center': 'Dendrogram (Centroid Linkage)'}>,
<Axes: title={'center': 'Dendrogram (Ward Linkage)'}>,
<Axes: title={'center': 'Dendrogram (Weighted Linkage)'}>]
ax = plt.subplot(111, xlabel='x', ylabel='y', title='title')
for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +
ax.get_xticklabels() + ax.get_yticklabels()):
item.set_fontsize(20)
# unused:
# https://stackoverflow.com/questions/49572582/distplot-got-an-unexpected-keyword-argument-figsize
# Solves figsize:
# https://stackoverflow.com/questions/31594549/how-to-change-the-figure-size-of-a-seaborn-axes-or-figure-level-plot
# plt.figure(figsize=(3,3))
# ax=plt.subplot(111, title='title')
# for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +
# ax.get_xticklabels() + ax.get_yticklabels()):
# item.set_fontsize(9)
# plt.subplot(figsize=(4,4))
# ax=plt.subplot(title='title')
# for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +
# ax.get_xticklabels() + ax.get_yticklabels()):
# item.set_fontsize(20)
fix bug: adjust text size:
as follows:
replace:
ax=sns.boxplot(data=df2, x="HC_segments", y=variable)
with:
ax=sns.boxplot(data=df2, x="HC_segments", y=variable)
for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +
ax.get_xticklabels() + ax.get_yticklabels()):
item.set_fontsize(15)
plt.figure(figsize=(30, 8))
plt.suptitle("Boxplot of numerical variables for each cluster")
for i, variable in enumerate(num_col):
plt.subplot(1, 12, i + 1)
ax=sns.boxplot(data=df2, x="HC_segments", y=variable)
for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +
ax.get_xticklabels() + ax.get_yticklabels()):
item.set_fontsize(15)
plt.tight_layout(pad=2.0)
plt.gcf().number
1
You compare several things, like:
You can also mention any differences or similarities you obtained in the cluster profiles from both the clustering techniques.
Estimated Notebook Runtime: 2m 19s